.p . ..V:.I.. 5 If 3:: :1: . : :9}: .1 ;u .. I .l {a o . r a 5n: p 4 ea ,1 9|; “v.1, 15...; it. .1 vet}. I .1. 5. P5... Ab... . 91!: A. v (.5 (13..., .15 “an” if x......, 4...“: a A .33.}, Iran? anew ‘n . .. S 3.3: 3.. #14 g 3 ”m; 3:5,u, Iv .i.‘n £511.12. C}. ...’u‘ 53- l tin-1.“. huxaofc... A ,3?! .3. 3;: p. . :1... 5;: .3...» ‘ I. .. ,0 .1 '1‘ I ,r: .3 x.“ .4 v ‘ .F .3 .9? J S . a E: .. . ..%mr€.§ 1 33?: 2,? ruff! :3. 3»... I. ‘1‘: fl. I..v.r)la...!r 1:33 . 2'5. 1:... l.) .l‘ '3 9 . .1}; 3.) .a :3..- 3 5,.» .vu) .z fiuld .105 UNIVERS SITYLBIEIRAR "‘5 III'IIIIIIIIIII‘III‘II III IIIIIIIIIII ,. 3129301388 ((4.? .I7 This is to certify that the dissertation entitled Determinants and Outcomes of HIV Educator Communication Networks presented by Gary Steven Meyer has been accepted towards fulfillment of the requirements for Ph.D. degree in Communication Major profeIsor Date 11/17/95 MS U is an Affirmative Action/Equal Opportunity Institution 0-12771 LIBRARY Michigan State Unlvers'ty PLACE ll RETURN BOX to remove thb checkout from your record. TO AVOID FINES mum on or before date duo. DATE DUE DATE DUE DATE DUE # I IIi—Ij DETERMINANTS AND OUTCOMES OF HIV EDUCATOR COMMUNICATION NETWORKS By Gary Steven Meyer A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Communication 1995 HI'I/fi PTOgI PIEVE the t léile themi Itasc‘ 3X and E ABSTRACT DETERMINANTS AND OUTCOMES OF HIV EDUCATOR COMMUNICATION NETWORKS BY Gary Steven Meyer This study examines communication networks of HIV educators in the state of Wisconsin. Specifically, this investigation considers the impact of social differentiation and geographic proximity on interorganizational linkages of HIV educators and the subsequent use of program planning strategies. Telephone interviews were conducted with administrators of HIV/AIDS organizations to identify the population of HIV prevention programs in Wisconsin. Thirty-nine organizations operating 65 prevention prpgrams with a total of 75 educators were identified through the telephone interviews. Following the telephone interviews, a written questionnaire was mailed to each HIV educator to measure the extent of similarity among them. At the same time, the relative physical distance between programs was determined. Based on these results, HIV educators were placed in a 2 X 2 matrix representing social differentiation (similar/dissimilar) and geographic proximity (close/far). in me: gre the com; 5051:. impor outsi With Thirty-three HIV educators then tracked interactions related to HIV programming by completing communication network logs. Nine-hundred fifty logs were completed over a nine—week period. Participants also completed a written questionnaire designed to measure the extent to which they would incorporate nine aspects of the Centers for Disease Control and Prevention's Health Action Model in their HIV programming. Study results suggest that geographic proximity has a significant impact on communication networks. More proximate HIV educators spent more time interacting through richer communication channels with a greater number of better sources of information than did HIV educators that were less proximate. Social differentiation had little impact on communication networks suggesting that social barriers such as age, education level, ethnicity, and sexual orientation are perceived as relatively unimportant in interactions concerning HIV prevention programming. Results further suggest that HIV educators with relatively better communication networks perceive (1) evaluation to be relatively more important and (2) environmental scanning and participation from others outside the program as relatively less important than do HIV educators with relatively poorer networks. Copyright by GARY STEVEN MEYER 1995 To Anne: Without whom this could not have been possible. close DECEE QDCO; Item; ACKNOWLEDGMENTS I would like to thank my entire family, especially my parents, and close friends whose continual support over the years has given me the necessary strength to realize this goal. I thank Dr. James W. Dearing my dissertation chair, mentor, and friend who provided me with so many learning opportunities. His untiring patience and absolute confidence enabled me to grow as a person and a scholar. I will always be indebted to him for all that he has taught me. I owe much to my dissertation committee, Dr. James W. Dearing (chair), Dr. Charles K. Atkin, Dr. Frank A. Fear, and Dr. Kim Witte for their guidance and support throughout the dissertation. I am indebted to them for believing in me and for allowing me to pursue this multifaceted investigation. I would like to acknowledge Dr. Cynthia Fridgen who first encouraged me to pursue a doctorate. I am further grateful for her tremendous support ever since. I would like to thank Dr. Ronald Tamborini who helped me refine the research design, and to Ms. Mary K. Casey who helped me solidify the data analysis. I would also like to thank Dr. Kurt Ribisl and Ms. R. Sam Larson whose provocative discussions helped improve this investigation. vi "HA €035 for IOC Mr. 10?: 0. Dr .1. I would like to gratefully acknowledge Dr. Everett M. Rogers for the many valuable lessons he has taught me over the past four years. His tremendous dedication to the scholarly enterprise serves as a constant source of inspiration. I would like to express my gratitude to the United States Agency for Health Care Policy and Research for providing the necessary funding to carry out this investigation. I would especially like to recognize Mr. Terry Shannon and Mr. Julius Pellegrino for their support. I am indebted to each and every respondent who took valuable time to assist me in this project. I wish each one the very best in the important work they do. Finally, I would like to thank my wife, Anne, for her tremendous love and support. I am grateful to her for being so understanding and for making so many sacrifices on my behalf. I am sure I can never begin to repay her. vii LIS' TABLE OF CONTENTS LIST OF TABLES. xi LIST OF FIGURES . . xiii I. INTRODUCTION. 1 A. HIV/AIDS in Wisconsin . . . 3 B. Significance of the Study . . 4 C. Theoretical Framework for the Study . 5 D. Research Questions and Hypotheses . 7 E. Delimitations of the Study. 9 F. Definition of Terms . . 12 G. Summary . 14 II. REVIEW OF THE LITERATURE. 16 A. Overview of the Theory and Research Literature Related to Networks. . . l6 1. Organizations . l6 2. Communication Networks. 17 3. Network Links . l9 4. Types of Interorganizational Relationships. 22 5. Communication Networks In Community- -based Programs. . 24 6. Social Differentiation as a Predictor of Communication Networks. 27 7. Geographic Proximity as a Predictor of Communication Networks. . . 29 8. Social Differentiation and Geographic Proximity as Predictors of Communication Networks . 3O 9. Other Determinants of Communication Networks. 32 B. Overview of the Theory and Research Literature Related to the Health Action Model . . . . . 32 1. Overview of the Health Action Model . 32 2. Components of the Health Action Model . . 33 3. The Health Action Model as Recognized Standard Practice. . . . . . . 39 C. Contribution to the Current Literature. 42 viii S". T. ‘1‘ . TI VIA TABLE OF CONTENTS LIST OF TABLES. . . . . . . . . . . . . . . . . . . . . . . . . . . xi LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . xiii I. INTRODUCTION. 1 A. HIV/AIDS in Wisconsin . . . 3 B. Significance of the Study . . . 4 C. Theoretical Framework for the Study . 5 D. Research Questions and Hypotheses . 7 E. Delimitations of the Study. 9 F. Definition of Terms . 12 G. Summary . 14 II. REVIEW OF THE LITERATURE. . . . . . . . . . . . . . . . . . . 16 A. Overview of the Theory and Research Literature Related to Networks. . . . . . . . . . . . . . . . . . . . . . . 16 1. Organizations . . . . . . . . . . . . . . . . l6 2. Communication Networks. . . . . . . . . . . . . . l7 3. Network Links . . . . . . 19 4. Types of Interorganizational Relationships. . . . 22 5. Communication Networks In Community- -based Programs. . . . . . . 24 6. Social Differentiation as a Predictor of Communication Networks. . . . . . . . . 27 7. Geographic Proximity as a Predictor of Communication Networks. . . . . 29 8. Social Differentiation and Geographic Proximity as Predictors of Communication Networks . . . . . 30 9. Other Determinants of Communication Networks. . . 32 B. Overview of the Theory and Research Literature Related to the Health Action Model . . . . . . . . . . . 32 1. Overview of the Health Action Model . . . . . . . 32 2. Components of the Health Action Model . . . . . 33 3. The Health Action Model as Recognized Standard Practice. . . . . . . . . . . . 39 C. Contribution to the Current Literature. . . . . . . . . 42 viii V_ III. IV. RESEARCH DESIGN . A. B. General Method. . . Specific Data Collection Procedures . . 1. Determining the Population of Organizations . 2. Determining the Population of HIV Prevention Programs and Educators. 3. Determining Social Differentiation and Geographic Proximity . . Selecting the Individuals of Study. Quantitative Network Log. . Quantitative Programming Questionnaire. Pretesting Data Collection Instruments. Treatment of the data . Limitations . \IO‘U‘b RESULTS . Program Identification and Description Survey . Demographics Questionnaire. 1. Social Differentiation. 2. Geographic Proximity. . . 3. Placement of Individuals within Cells . Communication Network Logs. 1. Summary Statistics. 2. Correlation Coefficients (Interaction Level of Analysis . 3. Correlation Coefficients (Individual Level of Analysis . . . . . . . . . . . Analysis of Variance. rogramming Survey. . . Health Action Model Questionnaire Items . Correlation Coefficients. Regression Analysis . Results Summary . J-‘wNH'UI-\ DISCUSSION. Overview. . . The Impact of Social Differentiation on Communication Networks. . . The Impact of Geographic Proximity on Communication Networks. . . The Relative Impact of Geographic Proximity and Social Differentiation on Communication Networks. Individual Cell Differences . Health Action Model Components. . The Relationship between Communication Networks and Components of the Health Action Model . 1. Inclusion of the Health Action Model in HIV programming. ix 43 43 44 45 46 47 49 51 53 53 54 55 58 58 60 62 65 66 68 68 84 93 100 103 104 107 113 117 122 122 124 126 133 134 138 138 138 i?“ . - r' L04 5251.} 2. The Impact of Communication Networks on HIV Programming . VI. RECOMMENDATIONS . Geographic Proximity. Social Differentiation. Miscellaneous . Future Research . UOUJ> APPENDICES Appendix Appendix Appendix Appendix Appendix meanest» BIBLIOGRAPHY. 139 141 141 145 147 149 151 153 158 162 163 167 Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table Table 10. 11. 12. 13. 14. 15. 16. 17. LIST OF TABLES Organizational type of HIV prevention programs. Program targeting characteristics . Determination of social differentiation scores. Quartile division based on social differentiation scores. Quartile divisions based on geographic proximity scores. . . . . . Communication log summary findings. Mean scores by channel type and cell placement. Mean scores for facsimile and letter interactions . Communication log individual level mean scores. Social differentiation correlations (interaction level) Geographic proximity correlations (interaction level) Network variable correlations (interaction level) Social differentiation correlations (individual level). Geographic proximity correlations (individual level). Mean scores for 20 Health Action Model items. Mean scores for nine aspects of the Health Action Model . Health Action Model correlation coefficients. xi 59 61 63 64 65 69 74 76 80 85 86 87 94 95 105 105 108 List of Tables (cont'd). Table 18. Summary of results. . . . . . . . . . . . . . . . . . . 118 Table 19. Network variable mean scores for all respondents. . . . 128 Table 20. Network variable mean scores excluding AIDS Service Organizations. . . . . . . . . . . . . . . 131 xii Figure Figure Figure Figure Figure LIST OF FIGURES State of Wisconsin map. Health Action Model Distribution of HIV educators . Characteristics of audiences targeted by prevention programs. Educator placement grid . xiii 11 34 50 59 67 It in the s (1) pred staff wt geograph communic. Programs In' design a: What the Carefull; Strategié either 1e m, | design in Used effe resourCes an “Sm: tOlerance programs dependent ‘ Q Determinants and Outcomes of HIV Educator Communication Networks I. INTRODUCTION This dissertation examines communication networks of HIV educators in the state of Wisconsin. Of primary concern in this investigation is (1) predicting communication networks of program administrators and staff who are relatively more or less socially differentiated and geographically proximic and (2) predicting the impact of differential communication networks on the development of HIV prevention education programs. Individuals involved with HIV prevention interventions strive to design and implement the best possible programs. But how do they know what the best program is? How do they learn to plan the intervention carefully and effectively? How do they know which of many potential strategies to incorporate into the design? In some cases, program staff either learn slowly, by trial and error, or they never learn at all. In other cases, program administrators purposively and deliberatively design interventions by incorporating specific strategies that have been used effectively in other campaigns. With continually shrinking resources, a move toward increased accountability in public health, and an urgent need for effective HIV prevention campaigns, there is less tolerance for programs that do not build upon lessons from previous programs. As a result, program survival is becoming more and more dependent on careful and strategic planning. While program personnel may obtain information about program 1 2 design from a number of sources (e.g. conference presentations, books, peer-reviewed journal articles, newsletters), they are most likely to obtain information through formal or informal interactions with other HIV educators within local or state departments of public health or community-based organizations (Goldstein, 1995). The precise means by which information is transferred and the HIV educators most likely to transfer or obtain information is not well known or documented. Two models of information dissemination, one based on social characteristics (social differentiation) (Rogers, 1983) and the other based on spatial factors (geographic proximity) (Hagerstrand, 1967), provide different explanations for the flow of information between individuals. The relative and combined impact of each dissemination model is investigated in the present dissertation. What outcomes are associated with differential communication networks? In the present study, the impact of varying communication networks on program development is considered by looking at the extent to which aspects of the U.S. Centers for Disease Control and Prevention (CDC) Health Action Model (Jorgensen, McKenna, & Kingon, 1994) might be incorporated into the design of HIV prevention programs. The Health Action Model is rooted in social marketing and community planning and incorporates many program activities that have been successfully incorporated in health campaigns around the world for the past three decades (Kotler & Roberto, 1989). Acqui Ilcst seriou 306.000 ind and two to ‘ immodefic (Centers for become the I of 25 and 45 the world. ] tine, therel efficacious epidemic. Among 33th from t? POPIllation; The first A fates incre; 19905. As! mIECIion' é heme inf e reWted fr,| A. HIV/AIDS in Wisconsin Acquired immunodeficiency syndrome (AIDS) has become one of the most serious public health concerns of recent times. Presently, about 500,000 individuals in the United States have been diagnosed with AIDS, and two to three times that number are believed to carry the human immunodeficiency virus (HIV), the infectious agent that causes AIDS (Centers for Disease Control, 1994). In the United States, AIDS has become the leading cause of death among all Americans between the ages of 25 and 45 (National Center for Health Statistics, 1994), and around the world, 17 million people are estimated to have HIV. At the present time, there is no known cure for AIDS and no vaccine for HIV. As such, efficacious HIV prevention education is critical in order to slow this epidemic. Among other states and Puerto Rico, Wisconsin ranks approximately 35th from the highest in numbers of AIDS cases (rate per 100,000 population) (Wisconsin HIV Prevention Community Planning Council, 1994). The first AIDS case in Wisconsin was diagnosed in 1982. HIV infection rates increased during the 19808, but have declined moderately in the 19905. As of October 1995, 5,111 individuals in Wisconsin had HIV infection, and of these 3,051 met the CDC criteria for AIDS (Wisconsin AIDS/HIV Program, 1995). While individuals all over Wisconsin have become infected with HIV, about two-thirds of HIV cases have been reported from Milwaukee County which includes the city of Milwaukee and Dane County which includes the city of Madison. Men who have sex with men account for nearly two-thirds of the 12 percent I make up abc; accounts fc: have been i: African Ire: comparison. The t‘ relative 1: HIV educate, certain pro Effective i understand; coImmicati itformation °f a health likelihood SPQCific pr of the Pub} the U.S. Ag 5051 Of red seh’iCes f0 ““51 areas 4 AIDS cases in Wisconsin. Injection drug users account for approximately 12 percent of reported AIDS cases and individuals with both risk factors make up about another five percent of AIDS cases. Heterosexual contact accounts for six percent of AIDS cases. Most of the individuals that have been identified with HIV infection in Wisconsin are White, however, African Americans and Hispanics are disproportionately affected in comparison. B. Significance of the Study The theoretical significance of the present study concerns the relative impact of social differentiation and geographic proximity on HIV educator interorganizational linkages and the subsequent use of certain program planning strategies which have been shown to be effective in past health campaigns. This study will enhance understanding of the determinants and outcomes of health providers' communication networks (Walker, 1992), serve as a test of two predictive information dissemination models, and explore the use of certain aspects of a health planning model. Information pertaining to the relative likelihood of communication between individuals and resulting use of specific program planning strategies will serve the pragmatic interests of the public health field. The U.S. Public Health Service as well as the U.S. Agency for Health Care Policy and Research, each stress the goal of reducing health disparities and improving access to preventive services for all Americans, especially minorities and individuals in rural areas (U.S. Agency for Health Care Policy and Research, 1990, pr 1992. 19931 which are i The 1 two diffusi patterns be irfornatior geographic comnicate social syst first model diffused as are more 11] Similar or I Bhomik, 19- “ulnicate example, ea Sane “Eli/er: an overall I COMiCatE extreme hon Smup Similz WP. and . 5 1992, 1993; U.S. Department of Health and Human Services, 1990), both of which are examined here. C. Theoretical Framework for the Study The present dissertation may be conceptualized first as a test of two diffusion models, one which predicts information dissemination patterns based primarily on social factors and the other which predicts information dissemination patterns based primarily on spatial or geographic factors. Diffusion is the process by which an innovation is communicated through certain channels over time among members of a social system. (Hagerstrand, 1967; Rogers, 1983). According to the first model, communication networks are developed and information diffused as a result of social factors. Accordingly, two individuals are more likely to form a link and exchange information if they are similar or homophilous to each other in important ways (Rogers & Bhowmik, 1971). Homophily is the degree to which individuals who communicate with each other are similar. 'Two health practitioners, for example, each of whom earned a Master's degree in Public Health from the same university, are likely to share many beliefs and values regarding an overall approach to health promotion. As such, they are likely to communicate with each other in health program planning. Cases of extreme homophily are characterized by a high degree of perceived in- group similarity and concomitant perceived out-group difference. Frequent communication occurs between homophilous members within a group, and little or no communication with heterophilous others outside the group. if they are because the individuals that they I. offices dir great deal behigh. C Seographic individuals states to n “creases, This ( networks! 51 networks “I strategies. design the into the lit per50ml ml other inter conSidEred and recent the group. The second diffusion model predicts information dissemination based on spatial rather than social factors. According to this model, two health practitioners are more likely to communicate with each other if they are proximic or physically close to each other, rather than because they have common social characteristics. The closer two individuals are physically located to each other, the more likely it is that they will communicate and share information. Two individuals with offices directly across the hall from each other are likely to share a great deal of information because the frequency of contact is likely to be high. Communication and information sharing is likely to decrease as geographic proximity increases so that as physical distance between individuals increases (e.g. from buildings to cities to counties to states to regions, and so forth), the likelihood of interaction decreases. This dissertation also tests certain outcomes of communication networks, specifically the extent to which differential communication networks impact the potential use of Health Action Model planning strategies. Since program personnel primarily learn about program design through interactions with others, this study will provide insight into the impact of communication networks on the extent to which program personnel may use strategies that have been successfully incorporated in other interventions. Aspects of the Health Action Model may be considered "recognized standard practices" as a result of their extensive use in successful program planning over the past three decades Iand recent adoption as a model by the U.S. Centers for Disease Control 7‘ and Preventi Specif itI'estigatiII Research 0114 Ilhat interorgani Research Q1,- I'hat COM-Inicati Research 0‘. ill-at EEOgraphic and Prevention. D. Research Questions and Hypotheses Specific research questions and hypotheses that guide the investigation are presented below. Research Question 1 What impact does social differentiation have on interorganizational communication networks of HIV prevention educators? Research Question 2 What impact does geographic proximity have on interorganizational communication networks of HIV prevention educators? Research Question 3 What is the relative impact of social differentiation versus geographic proximity on interorganizational communication networks of HIV prevention educators? Hrgcthesis HI'I' geographic less deter other HIV . Hypothesis HEY l geographic; developed j Prevention Breechesrs HIV 1 geographies. Educators. commdnicat; dissimilar geographic; Hypothesis 1 HIV prevention educators who are both dissimilar to, and geographically distant from other HIV prevention educators will have less developed interorganizational communication networks than will other HIV prevention educators. Hypothesis 2 HIV prevention educators who are both similar to, and geographically close to other HIV prevention educators will have more developed interorganizational communication networks than will other HIV prevention educators. Hypothesis 3 HIV prevention educators who are either dissimilar to, but geographically close to other educators 91 who are similar to other educators, but geographically distant, will have interorganizational communication networks that are developmentally between those who are dissimilar and geographically distant and those who are similar and geographically close. Hypothesis 4 HIV prevention educators who have more developed interorganizational communication networks will potentially incorporate more Health Action Model activities in programming efforts than will HIV prevention educators who have relatively less developed communication networks. Research Question 4 What impact will relatively more or less developed communication networks of HIV educators have on the perceived importance of Health Action Model programming activities? E. Delimitations of the Study The following delimitations of the investigation are acknowledged. First, the study focuses only on health educators who work in the substantive area of HIV prevention education. This single group of education specialists was selected because (1) the diversity of audiences targeted for education is ideal for comparisons involving social differentiation and geographic proximity, (2) there exists an urgent need to learn as much as possible about effective HIV prevention programming, and (3) the investigator is familiar with many aspects of the disease including its history, demographics, and epidemiology, and has experience working with HIV prevention administrators and staff. A sec, prevention i 'u‘isconsin (F reasons. F1 prevention ; including 1. several Nat‘ city of 511' American cc: rated the 7 raised it f» Po 9 he n' nt‘al r afe- . «lllated Prel' ; lm¢na r). 10 A second delimitation concerns the subjects of study. All HIV prevention educators included in the study work in the state of Wisconsin (Figure l). Educators from this state were selected for two reasons. First, Wisconsin has a large number of urban and rural HIV prevention programs. The State also has diverse ethnic representation including large African American and Hispanic communities, as well as several Native American communities in rural Northern Wisconsin. The city of Milwaukee also has one of the nation's most segregated African American communities (Farley & Frey, 1994). In 1990, Milwaukee was rated the 7th most segregated city in the United States, a ranking which raised it from the 14th most segregated city in 1980 (Farley & Frey, 1994). Based on these demographics, a wide variety of HIV educators was anticipated, including educators in urban and rural areas and educators who are relatively similar and dissimilar to other educators. The state of Wisconsin was also selected because the investigator is familiar with the State. The investigator was raised in Milwaukee, graduated from the University of Wisconsin-Madison, and has family and friends located throughout the state. This support network provided a resource base that enabled efficient and cost-effective data collection. A third delimitation of the study is that not all HIV prevention educators within the State of Wisconsin participated in the study. Potential respondents were eliminated from inclusion because they were affiliated with a relatively new program (less than one year old) or because the organization was too large (more than 10 employees). Other individuals were excluded if they failed to respond to one of the preliminary questionnaires. WISCONSIN Figure 1. State of Wisconsin nap. 12 F. Definition of Terms Definitions of key terms are provided alphabetically below. Communication Network A structured set of individuals linked together by patterned flows of information (Farace, Monge, & Russel, 1977; Rogers & Kincaid, 1981). Community-based Program A locally-implemented initiative carried out by an organization that is intended to inform, persuade, or mobilize a local target audience (Rogers & Storey, 1987). Diffusion The process by which an innovation is communicated through certain channels over time among members of a social system. (Hagerstrand, 1967; Rogers, 1983). Geographic Proximity The amount of physical distance between two or more entities. 13 Health Action Model A program planning process developed by the United States Centers for Disease Control and Prevention (Jorgensen, McKenna, & Kingon, 1994). The planning model is based on aspects of social marketing and community planning. Heterophily The degree to which individuals who communicate with each other are dissimilar (Lazarsfeld & Merton, 1964; Rogers & Bhowmik, 1971). HIV Prevention Program An organizational health initiative comprised of coordinated activities that provide information about HIV transmission and prevention to individuals. Homophily The degree to which individuals who communicate with each other are similar (Laumann & Pappi, 1976; Lazarsfeld & Merton, 1964; Rogers & Bhowmik, 1971). 14 Innovation An idea, practice, or object that is perceived to be new by the adopter (Rogers, 1983). POpulation Group Uniqueness The degree to which a social network of relatively homophilous persons is different from the larger social system of which it is a part (Dearing, Meyer, 6 Rogers, 1994). Social Differentiation The extent to which two or more individuals or groups are similar to each other based on certain social characteristics. G. Summary This investigation focuses on the impact of social differentiation and geographic proximity on interorganizational communication networks of HIV educators in the state of Wisconsin. The study further examines the extent to which resulting communication networks impact the perceived importance and potential use of specific program planning strategies. The strategies of primary interest are derived from the Health Action Model, a program planning tool developed and adopted by the U.S. Centers for Disease Control and Prevention. 15 This investigation is expected to yield both theoretical and applied outcomes. First, the impact of social differentiation and geographic proximity on the diffusion of information will be ascertained. This investigation will determine which model of information dissemination better predicts communication networks and flows of information between HIV prevention educators. Additionally, the impact of differential communication networks on the use of recognized standard practices incorporated within the Health Action Model will be ascertained. Health educators may use information about interorganizational networks to cultivate linkages that enable them to become part of the network of organizations implementing the most widely recommended strategies for program success. II. REVIEW OF THE LITERATURE A. Overview of the Theory and Research Literature Related to Networks 1. Qrganizatisns Many organizational theorists have come to view organizations from a systems perspective (Katz and Kahn, 1966; Parsons, 1956). From this perspective, organizations are seen as open systems which must interact with their environment, including other organizations in order to survive. As a result of the mutual dependence between organizations, interorganizational research has become the subject of many studies. Early on, most interorganizational studies focused on a single organization and those organizations that interacted directly with the focal organization (Dill, 1958; Warren, 1967). This type of network study became known as an egocentric network study (Burt, 1980; Mitchell, 1969). More recently, some studies have moved away from focusing on single organizations, to consideration of the entire network occurring between all organizations (Aldrich, 1978; Benson, 1977). The present dissertation is based on the former type of study, the egocentric network study, focusing specifically on aggregated links of organization members, that is, on egocentric networks of program personnel. 16 17 The study of communication networks has become the focus of much research within the discipline of communication. As Whetten (1981) notes, however, the phenomenon of interorganizational relations has attracted scholars from a variety of other disciplines including Public Administration, Marketing, Economics, and Sociology. Jablin (1980) suggests it is one of two major perspectives for studying organizational communication, and Rogers and Kincaid (1981) believe it is an emerging paradigm for the study of human communication. One of the most visible and important recent studies involving social networks concerned the early identification of the first network of individuals infected with HIV (Klovdahl, 1984, 1985). Since the etiology of the disease was unknown early on, Klovdahl's network study served as a beginning point . from which to determine the basis for the spread of the infectious agent. A communication network is a structured set of individuals linked together by patterned flows of information (Farace, Monge, & Russel, 1977; Rogers & Kincaid, 1981). Communication networks are important because an individual's behavior is, in part, a function of the networks in which the individual is a member (Korzenny & Farace, 1978; Logan & Molotch, 1987). While individual attributes are certainly important in defining human behavior, social theorists argue that individual attributes provide only a partial account, and further that they cannot account for certain group and social phenomena (Blau, 1982; Merton, 1975). 18 Hyman first noted the importance of the reference group, which may be defined as a real or imagined group against which one refers when forming attitudes and behaviors (Merton, 1988). Durkheim (1951), also noted the importance of interpersonal network relations as an explanation for ”egoistic" suicides. Individuals who committed this unsocial act supposedly did not have the type of interpersonal network that would have prevented the suicide. In a classic study of physician networks, Coleman, Katz, & Menzel (1966) found a highly interconnected network of physicians who gathered information and advice from a wide range of sources and used it to update their patient care practices. Other health care innovation diffusion studies have also noted the importance of professional collegial networks for transferring information (see, for example, Greer, 1988; Manning & Denson, 1980; and Mytinger, 1968). Crane (1972) studied ”invisible colleges” which are networks of individuals within the same profession who are geographically distant from each other. Among academicians, her results suggest that while professors do maintain local networks, the invisible college is important for long-term success. O'Brien, Hassinger, Brown, & Pinkerton (1991) examined the relationship between the social networks of community leaders and the viability of rural communities with the expectation that leaders' extracommunity ties and relationships within the local area would have an impact on the viability of rural communities. Findings from this study were consistent with the hypotheses. In general, they found that linkages to individuals and organizations outside the community were associated with greater 19 community viability. Members of an organization form links with other individuals who are in the organization as well as others who are outside of the organization. Links with others inside the organization are referred to as intraorganizational links, and links with others outside the organization are referred to as interorganizational links. Within the organization, an individual may have links to one or more superiors, subordinates, or colleagues in the same department and at the same organizational level, or with members of other departments or organizational units. An individual may similarly have a variety of different links across organizations. As such, one's actions result from those networks established both within the immediate organizational setting and outside the organization. Roberts and O'Reilly III (1979) suggest that individuals devoid of communication networks within organizations are less likely than their peers to be satisfied and committed to the job. The focus of the present investigation is on networks established with individuals across organizational or programmatic boundaries. Interorganizational networks may be formal or informal, routinized or sporadic (Kimberly, 1981; Whetten, 1981). Formal links between organizations occur through newsletters, bulletins, letters, and program announcements (Klonglan, Warren, Winkelpleck, & Paulason, 1976) and with technological advancement, electronic mail serves the same purpose. 20 Informal linkages between staff members may occur while attending a variety of professional meetings, or at other local functions attended by both parties. Further, informal linkages may have developed even prior to organizational attachment (e.g. old school ties). Both formal and informal interorganizational links are important to consider because of the high potential each offers for information sharing. Early network studies focused primarily on the exchange of goods and material resources. Recently, however, studies have focused on information linkages (Eisenberg et al., 1985), a logical step given the importance information plays to the adaptiveness and survival of an organization (Wilensky, 1967). Although all individuals in an egocentric network form links to the focal person, each link is not necessarily of equal importance in the dissemination of information. Some network links or ties are considered to be weak (interorganizational links) while others are strong (intraorganizational links) (Duff & Liu, 1975; Granovetter, 1973; and Liu & Duff, 1972). Strong ties exist between individuals within the same primary network. These individuals are characterized by a high degree of homophily and are connected within the same interlocking personal network, that is, a network characterized by interaction among all dyadic partners. Weak ties, on the other hand, exist between individuals who are linked through radial personal networks which may be characterized as networks in which an individual interacts with others who do not communicate between themselves. Granovetter (1970, 1974) found that unemployed individuals were more likely to receive information about their eventual job from acquaintances (weak ties) than 21 from close friends (strong ties). He reasoned that individuals within the same personal network share the same information, but that individuals with infrequent contact share new information. In a comparable study, Lee (1969) traced the links that women went through to acquire information about abortions where abortions were illegal. She found that women tended to contact female friends of the same age rather than relatives or employers. Rogers and Kincaid (1981, p. 128) concur with these findings and suggest that ”the information-exchange potential of dyadic communication is related to the degree of heterophily between the transceivers.” As compared to strong ties, weak ties have the potential to be very important in diffusing information. Strong ties share similar information while weak ties are capable of transmitting information from one group to another that the other is not likely to have access to. Granovetter (1982) notes that not all weak ties are important in the dissemination of information, but rather only those that serve as bridges, because bridges connect two separate networks and provide the most direct route to information dissemination. The importance of weak ties is that they are disproportionately likely to serve as bridges than are strong ties. In a study of organizational ties, Friedkin (1982) similarly found that weak ties were more important than strong ties in accounting for information related to activities outside of the organization. He further suggests that weak ties are important, not because of the individual efficiency, but because of the large number an individual is likely to have. As a result of these findings, the focus of the present investigation is on interorganizational ties rather than 22 intraorganizational ties. Although a good deal of research has been conducted on interorganizational groups, much of the work focuses on joint relationships formed in order to achieve one or more explicit goals. Several individuals have reviewed the extant research in interorganizational relations (see for example, Aldrich & Whetten, 1981; Laumann, Galaskiewicz, & Marsden, 1978; Galaskiewicz, 1985; and Van de Van, 1976). Oliver (1990), in her review of interorganizational relations identifies generalizable determinants of formal relationship formation. Others have focused on a single type of interorganizational relationship. Provan, for example, has focused on various types of interorganizational relations within specialized networks. In one study, Provan (1983) focuses on federations and in another (Provan, 1984) focuses on decision-making within a multi-hospital consortium. Luke, Begun, & Pointer (1989) focus on the quasi firm within the health care industry, defined as a "loosely coupled arrangement created to achieve long-lasting and important strategic purposes" (p.9). While these thorough reviews and discussions of specialized networks enhance our understanding of a certain type of interorganizational relationship, they do not specifically address informal relationships between organizations, the focus of the present investigation. Little research has been conducted on informal interorganizational networks with a focus simply on the exchange of information between 23 individuals without an assumed formal linkage or ulterior purpose (Schopler, 1987). Schopler (1987, p.703) focuses on interorganizational groups which are "composed of members, representing parent organizations and community constituencies, who meet periodically to make decisions relevant to their common concerns, and whose behavior is regulated by a common set of expectations." She suggests these groups can be classified either as mandated or voluntary with high or low task structure. For the present investigation, however, even the voluntary group with low task structure is too formal. Weick (1976) classifies interorganizational relationships by the degree of coupling, that is, whether they are relatively more or less tightly or loosely coupled. Luke, Begun, and Pointer (1989) combine tightness of coupling (high/low) with degree of strategic purpose (high/low) in a 2 X 2 matrix to describe four types of interorganizational relationships. The interorganizational relationship with low tightness of coupling and low degree of strategic purpose is called a network and ”is formed between any two or more organizations that collaborate on activities that are not sufficiently important or long term to be considered strategic" (p.13). Eisenberg and others (1985) identify three linkage levels at which information (or resources) may be exchanged: Institutional, representative, and personal. At the institutional level, information is exchanged without the involvement of specific individuals, whereas at the representative level official organizational representatives conduct transactions. The personal level is characterized by information exchange between members of different organizations in a nonrepresentative or private capacity. These might occur through old 24 friendships or "old school" ties. These indirect and informal associations are important to consider in order to fully understand interorganizational relationships (Eckstein, 1977). Aldrich (1979) notes the importance of "kinship ties" which he suggests are extremely stable. Sarason & Lorentz (1979) suggest that under some conditions informal personal contacts may prove more successful to the organization than more formal approaches to interorganizational relations. The present investigation focuses on networks that are very informal, characterized by loose coupling (Weick, 1976), a low degree of strategic purpose (Luke, Begun, & Pointer, 1989), and where information is exchanged at the personal level (Eisenberg et al., 1985). Community-based organizations and their programs have unique characteristics which differentiate them from other types of organizations and programs. They are typically small in size, often with less than 10 full-time staff members. Many community-based programs have three or fewer full-time employees, and rely on the services of volunteers to maintain the viability of the organization (Bracht & Gleason, 1990; Cain, 1993). Community-based programs typically serve a narrow target audience because they usually arise in response to a specific unmet need among a narrowly defined audience (Thomas, 1990). Mann (1993, p.1379) suggests that those who are discriminated against by society are less likely to “receive information adapted to their needs, to have access to the range of critical health 25 and social services, and to be able to organize as a community." The community-based program thus carves a niche for itself as it begins to meet the previously unmet need. Individuals within community-based programs often do not have the level of program planning and management skills that individuals within other larger programs do. This is due, in many cases, to limited formal training and lack of previous programming experience. Given the urgency to meet the needs of the specified target audience, program personnel are often hired based on criteria such as knowledge of, and credibility among, the clientele served, and availability and willingness to work long hours for low pay, rather than experience in the programming area or a formal public health degree from an accredited university. As their name suggests, community-based programs have strong roots 'within their community and thus must work together with many other torganizations and groups of people. In short, they must work within a icollaborative environment. The move toward collaboration has been made in the United States (U.S. Department of Health and Human Services, 1990), Canada (Cain, 1993) and throughout the rest of the world (Delaney '6 Moran, 1991) as evidenced by the emphasis on collaboration in the ‘World Health Organization's Health For All by the Year 2000 initiative (Means, Harrison, Jeffers, & Smith, 1991). Van Beurden, Lefebvre, and James (1991) suggest that strategies employed by community-based programs require more interaction with the community than do strategies that are either individual-based or strictly media-based. Collaborating with multiple sectors of the community such as business, political, educational, religious, recreation, and community groups is a necessary 26 aspect of effective programming (Thompson and Kinne, 1990). Establishing networks that enable members to function better is important to all programs, but perhaps even more so within community- based programs, and especially those providing health services (Schopler, 1987). Interorganizational network links can provide valuable information about how to design, implement, and evaluate single interventions or larger ongoing programs. Walker (1992) suggests cognitive, affective and practical skills of workers are enhanced through collaboration and networking. Thompson & Kinne (1990) similarly note that networks between organizations facilitate diffusion of ideas and practices. Although programmatic information of this type can sometimes be obtained through other means, direct personal contact is the most effective and efficient way (Daft & Lengel, 1984; McKinney, Barnsley, & Kaluzny, 1992; Mintzberg, 1973; Tushman, 1978). Heathcote (1990) examined the role of networks in relation to professional development and support and found that the most frequently cited reason for becoming a participant in an interorganizational network was that the network provided a forum for the exchange of information, ideas, and experience. Participants also mentioned receiving and giving advice, discussing problems, and sharing resources as other benefits. Studies have typically shown that frequent communication with colleagues both inside and outside their immediate group is important to high project performance (Zenger & Lawrence, 1989). Information obtained through other, less direct, means may be problematic. Periodicals may provide information that is either too theoretical and burdensome, or in some cases already outdated and 27 replaced with more innovative and useful strategies, especially where the delivery of preventive HIV health programming is concerned. Information from dissertations may be too theoretical, difficult to access, or expensive to obtain. Newspaper articles, on the other hand, may be too simplistic, glossing over important programmatic details. Given the importance of interorganizational networks, a central question is, ”What factors or determinants are important in the development of interorganizational networks?" Numerous studies have addressed this issue, most focusing on one or more individual or organizational variables thought to be important. While these variables are important and should not be overlooked, the present investigation focuses broadly on two explanations of network development and information dissemination. One explanation is based on the reasoning that the extent of similarity along social factors accounts for the development of communication networks, whereas the other explanation is based on the reasoning that individuals in close proximity to each other will develop network ties. Each of these theoretical models, along with supporting evidence, is reviewed below. One theoretical perspective suggests that social differentiation plays an important role in the development of networks. Social differentiation is the extent to which two or more individuals are similar to each other based on certain social structural factors. EXP: 55C and 28 Rogers and Kincaid (1981, pp. 307-08) suggest that "social characteristics on which any two individuals are relatively homophilous, are important determinants of who interacts in a system." Fischer et a1. (1977) support this reasoning by proposing that individuals have a limited number of relationships possible and therefore tend toward relatively more homophilous relations. In a study examining the extent of homophily in interorganizational networks in human service organizations, Lincoln and McBride (1985) found evidence that "agencies with similar client racial makeups have more frequent exchanges" (p.28). Literature on organizational demographics suggests that within organizations, individuals tend to communicate with similar others (Kanter, 1977; Pfeffer, 1981, 1983). Galaskiewicz and Shatin (1981) examined characteristics of organizational leaders and linkage formation and found that in uncertain environments, organizational relations tend to exist more between leaders who had similar racial and ethnic backgrounds. Respondents in another study selected networks based on occupation level, education level, religious affiliation, and age (Fischer et al., 1977). Coleman, Katz, and Menzel (1966) also found age and religion to be important determinants among network links between doctors. In a study of New York high school students, the social characteristics of sex, race, and age were all important in determining network linkages (Kandel, 1978). A study of the diffusion networks of farmers suggests that those with similar levels of income, education, and innovativeness are important characteristics of who was linked to whom (Lionberger, 1975). Collins and Guetzkow (1964) further suggest that communication is typically directed to persons with the same .I- 531 III (‘9 in 'v‘. 29 socioeconomic status. Finally, studies by Kelley (1951), Thibaut (1950), and Stephan (1952) all support the general conclusion that status and prestige are important in determining interpersonal interaction. In the present investigation, social characteristics such as age, ethnicity, gender, and sexual orientation are all tested to examine their impact on communication networks. A number of studies have been conducted looking at the relationship between geographic proximity and network development. Rogers and Kincaid (1981) note that in nearly all network studies that have considered spatial distance, the general finding is that it is one of the main determinants of network linkages. In general, an inverse relationship exists between the physical distance separating individuals and the likelihood that they will be in the same network. Several studies have demonstrated this relationship within a variety of settings (Allen, 1993). Among office workers, Gullahorn (1952) found distance to be the most important variable in accounting for the interaction of 37 workers. Festinger, Schachter, and Back (1950) found that social visiting relations were more likely to develop within housing groups if the physical separation was small, and Powell (1952) had similar findings in a study of a Costa Rican village. In relatively isolated neighborhoods, studies suggest that the location of housing units are very important determinants of communication networks that develop 30 (Caplow & Forman, 1950; Merton, 1948). Within a specific interorganizational context, Whetten (1981) suggests that coordination between organizations will not occur unless they become aware of each other's needs and notes this can happen formally, informally, or through incidental contact resulting from geographic proximity (also see Reid (1969) and Schermerhorn (1975)). Only a few studies have'considered the relative importance of both social differentiation and geographic proximity simultaneously. Barnlund and Harland (1963) examined the communication networks of sorority women at a university campus in the Midwest. They found that physical factors, such as distance between buildings and passageways ‘were important in the initial development of interpersonal relations, but that social factors such as status eventually became more important in determining who maintained relationships with whom. Bochner, Buker, & McLeod (1976) conducted a study of 500 graduate students in Hawaii and found that sex and culture were the two main determinants of who was linked with whom. In this case, geographic proximity did not play a large role, perhaps because of the communal setting at the host facility (Rogers and Kincaid, 1981). In a review of small world studies, Bernard and Kilworth (1978) found that geographical proximity, occupational status, sex and race were the main determinants of network relationships. Parsons (1973) found a similarly strong effect for «.6... on, hut-.60»;- _. . .. 1.;E‘IS d . ‘ ‘ o =‘ be . .I‘ DID s-O,' :7” 43.1.13 4...,._ R 4‘". ed“ life ”v --, L: -- '2 IUEI 3.35251. '02:. (‘1 (1') r '9 31 geographical proximity in a study of one village in the Philippines, however, found little evidence for the importance of social variables such as socioeconomic status. Rogers and Kincaid reach the following conclusions related to the irueraction of both social differentiation and geographic proximity (Rogers and Kincaid, 1981, p. 312). a) Both geographic proximity and social homophily are important tieterminants of who interacts with whom, with spatial distance more ianortant in some situations and homophily on social characteristics more important in others. 'b) ‘When a system is relatively homogenous in social characteristics, Spatial distance will be the main determinant of who interacts with . EdmonL ‘3) Among equally close neighbors, homophily on certain social Characteristics is not a main determinant of who interacts with whom, 'bUt in longer distance links, social homophily is the main determinant of who is linked to whom. The question for the present investigation is whether social differentiation or geographical proximity plays a more important role in determining interorganizational linkages of HIV prevention educators? Does the urgency of their work, that is, the fact that educational programs revolve around an epidemic, encourage them to form networks . r ,. 1 *P' 5:355 6"" 3f intere: 3f the ne‘ :etth se :‘Oi ." ~ . “um“, vetting i 1161‘; {C 0C for it Spefiifica ."‘~Vn rlvé‘a'm 1| Prf'v'mtj 32 across either social barriers or physical space? Aside from social differentiation and geographic proximity, other factors, both individual and organizational, may be important cieterminants of the type of personal interorganizational relationships of interest within this investigation. Given the highly personal nature of the networks, for example, the number of years of experience in the ihealth services area, and especially experience directly related to the IiIV/AIDS may be important determinants. Individuals who have been 2 working in the health services area, and especially in the HIV area, are Ilikely to have made numerous contacts over the years that they may call Orlzfor information exchange. Similarly, the longer an organization and Specifically a program has been in existence, the more likely it is that Program personnel would have established personal contacts. B. Overview of the Theory and Research Literature Related to the Health Action.Model 1. Weds]. During the early 19903, the U.S. Centers for Disease Control and Prevention (CDC) developed and adopted a planning model for use in public health settings (Jorgensen, McKenna, & Kingon, 1994). This model, called the Health Action Model, has its philosophical . 'I". :1e:p:r:1...55 aged it for use. may be 3' I 52 applied to :‘iseese. virus atrcgran adrti effective prog 2:35 A. B. e gserelizabili q Istead. the i the planning I we successf: include: Envi: felineation, audience barr “5 Potential 55554 on feet “Pest of th: 33 underpinnings in commercial marketing practices, however, the CDC has adapted it for use in public health. Just as the commercial marketing model may be applied across product lines, the Health Action Model can be applied to any health intervention regardless of the specific disease, virus, or health problem. The Health Action Model cannot guide a program administrator through the design and implementation of an effective program. That is, the Health Action Model cannot direct inputs A, B, and C for desired outputs X, Y, and 2. Its generalizability would be severely limited if this were the case. Instead, the Health Action Model incorporates several essential steps in the planning process, that if adhered to, should result in relatively more successful programs. Following this model, a program plan should include: Environmental scanning, research and analyses, program goal delineation, analysis of potential prevention practices, analysis of audience barriers and benefits, analysis of product delivery, analysis of potential communication or promotion, evaluations, program changes based on feedback, and the inclusion of outside participation. Each aspect of the Health Action Model (Figure 2) is briefly reviewed below. 2. WWW Environmental Scanning The environment around the health intervention should be carefully scrutinized early in the process. According to Adams (1980), organizations require two kinds of external information. One type of momooan: m. mZ7UO-n I070>rnmrnx 1WOnmmm 185. >020: Zone. mowezfizmmm OCHGOZm ,, mZ<_mOZ7\..mZ._1>_. mfl>ZZ_ZD . . . 1329—320: 1392: Dow—m newsman: m. >315; . mcq<£=uzno _ZU_<=UC>_IM . Ins—E Sc: . Ins—=— _:..__n~.o_a . 0955.5. ”nuns-n: 2.: mag—2.5.5: . 105:5.— 13Z_ _ > . . -N>4._OZm ennmnmma Eahmvmfimse 9.25 93222.3 . 35.08 . mans.” . Ego . 55.559243 . Engaged . .156 v. . mno:o.=_nu ”mum—“MW.”— . hoaxes—5.... . 90 < . >hx § é Health Action Model. Fiésure 2 . fixation c: as the other :rgan'zation 1 environmental :araigns wit? gezeral group in potentia fight he dire: 3’? én‘irczmer i: the most 5'. 25355555 and :1 Research geared tOHard EititudES. be: this stage may ifltlwing r950 afpertinmt e E? ““9 from Hatchival l’e .ztmiews (Br 35 information concerning current decision making and policy formulation, and the other concerning unpredictable events that might impact the organization if they do occur. Following this reasoning, an environmental scan might include research about other local or national campaigns with a similar focus or other campaigns aimed at the same general group of individuals. Additional information may be sought about potential funding sources or existing or proposed legislation that might be directly related to a new intervention. In short, a scan of the environment should be undertaken to be sure the program is designed in the most strategic manner possible. Research and Analyses Research and analysis early in the program planning stage is geared toward an understanding of the target audience's knowledge, attitudes, beliefs, and behaviors surrounding the health issue. Data at this stage may be primary or secondary depending on a number of factors including resources allocated for such activities and the availability of pertinent existing data. Data may be quantitative or qualitative and may come from a number of different research activities and sources such as archival records, written questionnaires, focus groups, and personal interviews (Brewer & Hunter, 1989). (ru‘ ‘ P. C) (D b.) 8-... V 1 Ine prc; :‘siividuals ur its: is to be fired terms {1 test), as a gs avariety of c L: such a way then goal atte 3'91; National Cutter. 1984) £17515 Of P; At this bedeveloped 1 “celyses. and include many 1 product itsell stch as the 11 36 Program Goal Delineation The program's goals should be clearly stated so that even individuals unfamiliar with the topic or health area would know exactly what is to be accomplished through this program. Goals may be stated in fixed terms (i.e. 75 percent average competency on an HIV transmission test), as a gain (i.e. 25 percent increase in the use of condoms), or in a variety of other ways. Importantly, program goals should be stated in such a way that they are measurable. If they cannot be measured, then goal attainment cannot be known with certainty (Green & Kreuter, 1991; National Research Council, 1991; Windsor, Baranowski, Clark, & Cutter, 1984). Analysis of Potential Prevention Practices At this point in the process, potential prevention practices can be developed based on results from the environmental scan, research and analyses, and specification of program goals. Prevention practices may include many health actions and should be reviewed in light of the product itself. Consideration should be given to product attributes such as the intervention's effectiveness, cost-benefit ratio, complexity, and safety. 1-5'7515 Of A; .m Pctentie :raztice shou‘. m5: of tire ’heproduct, a Prince benef :onsideratior. vis a vis she: :sychosocial ': 351.7515 of P. Conside‘ alldience ’ 5 ac: in areas such. disSemination “1.1515 of P Comm £37115 of the Sxékers burg mgr” 80313 “Sage that I 37 Analysis of Audience Barriers and Benefits Potential audience barriers and benefits of each prevention practice should be analyzed. Consideration should be given to the amount of time and money it would take audience members to adopt and use the product, as well as related physical and psychosocial costs. Audience benefits should be similarly considered. For example, consideration should be given to the potential long—term cost savings vis a vis short-term expenditures for audience members, or perhaps psychosocial benefits for adoption, such as peace of mind. Analysis of Product Delivery Consideration here should be given to factors affecting the target audience's accessibility to the prevention practice. The foci here are on areas such as potential intermediaries that can assist with dissemination of the product, as well as delivery cost and the locations where the product or service might be offered. Analysis of Potential Communication or Promotion Activities Communication activities are considered in light of the specific goals of the program. Different communication strategies (mass media, speakers bureau, one-to-one counseling) may be utilized depending on Program goals or objectives. Further, the specific communication message that will be sent to the target audience again depends on :rcsral 505-15 general aware- a;;::priate. gersuasive me a;;:c riate. traction and p. n..gl..,"; tr. 'V-QUVL A Eliience res; T455 the 6x1. ECCGI ding to “Rain aCtiv P6511: sen'ic evaluation is Regan had a1 5e :‘iViCrs. Jéfa‘ ' ”dug Our, Program miside 81011;). 38 program goals. If, for example, the goal is to increase knowledge or general awareness, then an informational message may be most appropriate. On the other hand, if behavior change is the goal, then a persuasive message, perhaps with a fear appeal, might be most appropriate. Consideration should be given to the frequency of promotion and associated costs. Conducting Evaluations Three types of evaluation may be important: Formative, process, and outcome evaluation. The former is used to determine the best program and may be used continuously throughout program design, for example, to see which one of three particular messages the target audience responds best to. The second, process evaluation, is used to judge the extent to which the intervention has been implemented according to design. A process evaluation considers the extent to which certain activities intended to meet program goals and objectives (e.g. public service announcements) have actually occurred. Finally, outcome evaluation is used to measure outcomes or the extent to which the program had an effect on participant's knowledge, attitudes, beliefs, or behaviors. Obtaining Outside Participation Program personnel might obtain the support and participation of outside groups that may be influential in the adoption and use of the _:::iu:t among bythe Hcrld ngazization. setters {hens :rganizaticns leaders who a behaviors in Lazarsfeld, l Easement te £37.30} 0f PI mil idea c lager, 1975 h; sevEral at 193'. Jr kfebl'j 71:5,. mic healtl ‘A ~35“ " and b' 3:5; f‘ 'Lt 0f 1‘. 39 product among the target audience (Green, 1986; Green & McAlister, 1984). Participation by communities is also a principle highly regarded by the World Health Organization in health promotion (World Health Organization, 1986). Potential outsiders include target audience members themselves, as well as influential community members, and organizations. Influential community members may include opinion leaders who are able to informally influence others' attitudes or behaviors in a desired manner with relative frequency (Katz & Lazarsfeld, 1955; Rogers & Shoemaker, 1971). Although the Health Action Model is relatively new, many of its components have been widely recognized and used over the past 20 years in social marketing campaigns. Social marketing is a (1) social-change management technology (2) involving the design, implementation, and control of programs (3) aimed at increasing the acceptability of a social idea or practice (4) in one or more groups of target adopters (Kotler, 1975). While social marketing has been differentially defined by several authors (see for example, Fine, 1981; Novelli, 1984; Manoff, 1985; Lefebvre & Flora, 1988; Kotler & Roberto, 1989), it is distinguished by its consideration of nontangible products and improved public health, especially as related to disease prevention (Manoff, 1985), and by its emphasis on control of the change process through the conduct of research, planning, evaluation, and feedback. Kotler and s:::esstul Ca these have be " ' y. 9 “my; todas. “we: the last inccrcrated GJZSldE part} 313 comaniti Plfsued Consf ffiiback thy. Collabt Ha aim-.15 a has been Wank). De pru~Vn Ué‘ming Flaming in: SuggeSts in ”matron trad“1011.21 health Profe I‘ . A.\‘ svtia‘mratio 40 Roberto (1989) review and discuss a variety of relatively more and less successful campaigns based on social marketing principles. Some of these have been initiated by governments, others by citizens, some in highly industrialized countries, others in much less developed nations. Over the last two decades the social marketing model has been utilized extensively and has consistently been proven to be conceptually and pragmatically useful. One aspect of the Health Action Model that has not previously been incorporated within the social marketing framework is its emphasis on outside participation. The Health Action Model calls for program participation from individuals (target audience members), organizations, and communities. Of the three, only support from individuals had been pursued consistently in the past and typically their role was to provide feedback through formative evaluation of specific media or messages. Collaboration with organizations and communities as well as individuals is advantageous in the conduct of health promotion programs and has been advanced in other disciplines such as Sociology and Community Development for many years. Today, community-wide health programming is widely accepted (Thompson & Kinne, 1990). Of health planning initiatives, the U.S. Department of Health and Human Services suggests in Hegl;h1_£ggplg_ZQQQ, '...they require a high degree of cooperation and coordination between groups that are often not traditional partners: Environmental citizen groups and manufacturers, health professionals and churches, employers and hospitals. In support of this approach, Winnet, King, and Altman (1989) suggest that collaboration between researchers and community leaders is at the heart 35 gag-term : grc-gramati ~_:::fessionals prevention ef ::llabcratior. r various me iminduals f Ethic. the cc efforts. Th: eizcation pr; FTCETELmatic :cllaborate, EMMY new Such 35 Alins ”Me (1973 Because Prigran Plant “Pics, (3) F 5am adopted Zontrol an d I IECOgnizEd pl 15 HOt t0 5a a 41 of long-term maintenance of behavior change. They note that programmatic success is often dependent upon the collaboration of professionals, paraprofessionals, and volunteers. Collaborative health prevention efforts are advantageous for several reasons. In collaboration, health educators recognize and legitimize knowledge held by various members outside the organization. Collaboration allows individuals from many different organizations, groups, and sectors within the community to make valuable contributions to health education efforts. This is especially important for community-based health education programs where indigenous community knowledge is vital to programmatic success (Indyk & Rier, 1993). Often when local volunteers collaborate, program attendance rises. Collaboration also empowers community members, an outcome which has been advocated by individuals such as Alinsky (1946), Freire (1970), Schwebel, Kershaw, Reeve, Hartung & Reeve (1973), and Duhl (1986). Because the Health Action Model (1) has had its components used in program planning for many years, (2) is generalizable across health topics, (3) places an important emphasis on collaboration, and (4) has been adopted and recommended for use by the U.S. Centers for Disease Control and Prevention, aspects of it may be considered ”standard recognized practice” that should be considered by health planners. This is not to say that every health program should incorporate each component of the Health Action Model, but rather that the more aspects of the Model incorporated in the planning process, the more likely it is that the program will be successful. Since aspects of the Health Action Model have been successfully interporated educators vi: shculd be mo: of specific a consequence c The pre the r55€arch different Ex; diSSemthio] “3151 diere tESted theta 3“ “lame Provide imp- 113-ages {0 include thc those conta We'St'lgat 6.. :‘ provide it 42 incorporated into social programs over the past two decades, health educators with relatively more developed interorganizational networks should be more likely to have heard about them. As such, potential use of specific aspects of this model will serve as an outcome measure, or consequence of differential interorganizational network development. C. Contribution to the Current Literature The present investigation will make the following contribution to the research literature. First, this study will concomitantly test two different explanations for network development and information dissemination. While there have been a number of studies examining both social differentiation and geographic proximity in isolation, few have tested them together. This investigation will help provide evidence for the relative importance of one or the other. This study will also provide important information about informal interorganizational linkages forged by program personnel. The linkages considered will include those mandated through formal integration, but will focus on those contacts initiated and maintained voluntarily. Finally, this investigation will provide data about outcomes associated with differential communication networks. Specifically, this study will provide information about the perceived importance of Health Action Model activities and the potential for incorporation into HIV prevention program planning. 111. 355W A multi“ ,j’sreh'er & HUT-i Lientif}' the acre prc rams isaestionnaire . differentiati trcxiaity bet a;p:aach (Pr: into four 1m; This it $531ng ties Erlertaken or $1350“. & \‘a | | | :cliected fr: CC"-"‘5‘~lrlicatio.' idiom Either Ethod exnilnir Finall- 5351:1'29 the ‘ ta «tel-nine into the (1651 III. RESEARCH DESIGN A. General Method A multimethod research design was used in the present dissertation (Brewer & Hunter, 1989). A telephone survey was first conducted to identify the population of programs in Wisconsin that operate one or more programs focusing primarily on HIV prevention. A written questionnaire was then distributed to HIV educators to determine social differentiation and a series of maps was used to measure geographic proximity between programs and individuals. A ”most different systems" approach (Przeworski & Teune, 1970) was used to categorize HIV educators into four unique cells. This investigation focused on collecting data concerning boundary- spanning ties of organizational personnel, a focus which has been undertaken only infrequently in the past (Hall, Clark, Giordano, Johnson, & Van Roekel, 1977; Lincoln, & McBride, 1985). Data was collected from program personnel who completed network logs of communication events occurring over time (Rogers & Kincaid, 1981). Known either as egocentric, personal, or survey network data, this method examines individuals and characterizes networks surrounding them. Finally, a self-administered written questionnaire was used to analyze the perceived importance of Health Action Model strategies and to determine the extent to which specific strategies may be incorporated into the design of HIV prevention programs. 43 Data was. iztervie'u's. C ‘1 reestiomaires :cllect substa — _ — - e — — irfcraation s, :ec'ariques. advantage of Iizensate ft" Althoug Cr WESi-EXpe te‘ep‘hme. as trained them “‘45? Period Protocol 85:; RES‘éarch 1m. of iota-Vie“ | (La c011Ect dissertation respond‘Ent. es“semen en that intErVil ° . f confident 44 B. Specific Data Collection Procedures Data was collected in this investigation through telephone interviews, communication network logs, and self-administered written questionnaires.' This multimethod approach enabled the investigator to collect substantively different data through each method. Different data- collection methods were used because different types of information were obtained more validly and reliably using different techniques. Multiple research methods enabled the investigator to take advantage of the strengths of particular methods while at the same time compensate for weaknesses inherent in each (Brewer & Hunter, 1989). Although the investigation did not involve clinical, experimental, or quasi-experimental methods, the research did involve human subjects. The investigator interviewed HIV program administrators over the telephone, asked them to complete written questionnaires, and personally trained them to complete records of communication events during the study period. The conduct of all data collection followed interview protocol established by the Michigan State University Committee on Research Involving Human Subjects in order to assure the confidentiality of interviewees. After introductions but prior to the beginning of any data collection, the investigator (l) explained the goal of the dissertation, (2) explained why the interviewee had been chosen as a respondent, and the value of interviewee data to research goals, (3) estimated the amount of time that participation required, (4) explained that interviewee participation was voluntary, (5) explained the concept of confidentiality and guaranteed respondent confidentiality, (6) . v" :“d l .3519: ‘ . L termed . . The iisconsin Eisease C Rick'sille identif i :l'" 7 Y‘- a ;. “ list to i Ei'v'en thr- Vas Place State. p a.“ 45 provided interviewees with a business card or other identification as required by the interviewee, and if requested provided names of individuals at the Michigan State University whom they may contact with questions about the dissertation. 1. W The population of organizations conducting HIV/AIDS programs in Wisconsin was identified through the data base at the Centers for Disease Control and Prevention National AIDS Clearinghouse (1993), in Rockville, Maryland. The total number of organizations originally identified in this manner was 145 (National AIDS Clearinghouse, 1993). Four HIV prevention experts in Wisconsin then examined and updated the list to include 157 organizations‘. Experts contacted by telephone were given three business days to respond before a follow-up telephone call was placed. Each of the four individuals was specifically selected because of their unique knowledge concerning HIV prevention programming in Wisconsin. One individual was employed by the State Health Department and responsible for funding a wide array of prevention programs. The second individual was responsible for all HIV prevention activities at the organization providing the most HIV and AIDS-related programs in the State. Another individual recently completed a needs assessment in the State, and the final individual was knowledgeable about less well-known minority programs. 31:: to l mic.- 4;. .‘L:“..; ‘6‘ Pftgfams the organ FY€‘~‘erttic .‘ ... ‘0»;‘56d c 33': been :E'b'er the 46 2. ' ' e t' 0 V event'o s A letter introducing the principal investigator as well as the study was sent to a key staff person in each organization (Appendix A) prior to establishing telephone contact. Each organization that was identified as one that provides HIV prevention education was telephoned no less than five times. If a contact person could not be reached, or did not return the call, then the program was excluded from the study. Telephone interviews were conducted with an individual, typically an administrator, from each of the organizations providing HIV prevention programs in Wisconsin (Appendix B). The investigator first contacted the organization to determine if they administer one or more HIV prevention education programs where at least 50 percent of the effort is focused on HIV prevention among uninfected individuals, if the program had been operating for at least one year, and if the program employed fewer than 10 individuals”. If so, the investigator interviewed an administrator from the organization. The administrator was asked to describe each HIV prevention program operated by the organization that fit the aforementioned criteria. As each program was described, the investigator took notes about program goals and objectives in order to verify that the program was one that fit the criteria of the study. Programs with 10 or more employees, or programs that had not been in Two programs were excluded from consideration because they had not been operating for at least one year, and one program was excluded because it had more than 10 paid employees. A total of 12 individuals were excluded from the study based on these criteria. atsteme at for different iteration. ts At the iticriation r. Lrtlesentat i .: served as a t SE] provided links not ob: :rr . W prevent 1 c aMere fron adminiStered 47 existence at least one year were excluded from consideration to control for differences in organizational resources and length of time in operation, two potentially confounding variables. At the same time, information was obtained about the sources of information most important to each educator regarding the design, implementation and evaluation of their programs. This information (1) served as a reliability check for data collected through network logs, (2) provided additional information about interorganizational network links not obtainable through the network logs, and (3) served as a comparison for similar data already collected in a companion study of HIV prevention programs in San Francisco. Telephone surveys lasted anywhere from 15 minutes to one hour depending on the number of programs administered by the organization. 3. WWW Barium Social differentiation of program personnel was operationalized based on the extent of similarity or difference to other HIV educators in the sample. The criteria for determining differences among HIV educators included gender, age, ethnicity, education level, sexual orientation, length of time in the health field as well as specifically in the area of HIV prevention, and perceived homophily both with other HIV educators and other more general health educators around the State. Data were obtained through a self-administered written questionnaire mailed to all program personnel after obtaining permission from a ‘LU' "17w- ._.n ‘F_ j' I“: .uvL-‘V EZS'I'EI, E " .“ IO {Versus 5. “Sp-335,9 rESPCFIGe Iavised f to other C. 48 program administrator (Appendix C). A self-addressed, stamped envelope was included with the questionnaire to enhance the response rate. A social differentiation score was computed for all program personnel. Social differentiation scores were derived by summing "points" obtained on each characteristic. Depending on the respondent's answer, each individual either received "one point," "zero points," or in some cases "one-half point" or "one-quarter point" for each item. For items that measured variables continuously, such as age, points were assigned to those respondents who fell in the upper or lower quartiles. In these cases, fifty percent of the respondents therefore received ”points" and 50 percent did not. The same procedure was followed for items measured on a Likert-type scale. For items measured nominally, such as ethnicity and sexual orientation, "points" were assigned to any respondent who answered differently than the majority of other respondents. Scores were then summed for each individual who were then ranked from those with the highest scores, individuals most dissimilar to other HIV educators, to those with the lowest scores, individuals most similar to other HIV educators. Geographic proximity was measured in the following manner. After the population of programs had been determined, the location of each was marked with a push-pin or sticker on a state of Wisconsin map or other regional/local map depending on the number of programs in a particular geographic area. Rulers were then constructed representing distances of three miles, and 25 miles. To determine the relative extent of geographic proximity for any given program, each ruler was alternately rotated 360 degrees around the program marker. First, the ruler :etresenting :ther markers sane county ‘ooint' was tctal score four indivi: I’TOEIams we: Proxiaate ;; FICgrams , 49 representing three miles was rotated around the marker. The number of other markers within this radius was counted and assigned a value of three "points” each. Next, the ruler representing 25 miles was rotated around the program marker. The number of other markers within this radius, less those already counted, were assigned a value of two ”points." Programs falling outside the 25 mile radius but within the same county were assigned a value of one ”point," and finally one "point” was assigned for each program within the same organization. The total score for geographic proximity was then calculated by adding the four individual scores. This procedure was replicated for each program. Programs were then ranked from those with the highest scores, most proximate programs, to those with the lowest scores, least proximate programs. a. 52W Placement of HIV educators within a 2 X 2 matrix was determined by the educator's relative rank ordering for social differentiation and geographic proximity, measures designed to maximize within-cell homogeneity and between-cell heterogeneity. Educators who were close and similar to other HIV educators were placed in Cell A. Educators who were close and dissimilar to other HIV educators were placed in Cell B. Educators who were far and dissimilar to other educators were placed in Cell C, and finally, educators who were far and similar to other . educators were placed in Cell D (Figure 3). (an; as ch»- .IF. . "’°;76n9q n9 ruse». U, h; C . \ Figure 3 _ : Indus 10,, 0‘ of ti: Cells, app: LargEted Mi differential gIOupS' and DepartHIEnt I'ES 5 Stud‘, 50 Ge 'c ro 't Close Far A) Close D) Far Similar and similar and similar Social DiEE . I' B) Close C) Far Dissimilar and dissimilar and dissimilar Figure 3. Distribution of HIV educators. Inclusion of Ethnic and Gender Groups Of the 58 HIV educators originally placed into one of these four cells, approximately one-third (19) were affiliated with a program that targeted minorities, women, or both for HIV education. Because of the differential negative impact AIDS has had on certain ethnic minority groups, and in keeping in line with the objectives of the U.S. Department of Health and Human Services (1990) to provide preventive health education to all groups, specific inclusion of these individuals was a study prerequisite. After o visited each cases the lT‘.‘ it some case- atpointment. for cells, gercent agre farther was 223;". re the ::liected o'. 1°85 for ea.‘ “5h 0f the {C “White of any thIEe Tr- . siding Car 51 5. WW After categorizing the individuals for study, the investigator visited each individual to ask for their further participation. In most cases the investigator made an appointment with program staff, however, in some cases the investigator contacted HIV educators without an appointment. Of the 58 HIV educators originally placed into one of the four cells, 37 agreed to keep track of communication interactions (64 percent agreement rate). Each individual that agreed to participate further was trained for approximately 45 minutes on how and when to complete the communication logs. Communication network data was collected over a nine-week period. HIV educators were given color-coded logs for each of three data-collection periods (Appendix D). During each of the three data-collection periods, HIV educators were instructed to complete up to 25 logs. If 25 logs were completed prior to the end of any three-week data-collection period, HIV educators were instructed to stop and continue again at the beginning of the next data-collection period. If educators did not complete 25 cards during a three-week period, they were instructed to leave the remaining cards blank for that time period and move on to the next set of communication logs. Each educator was given a written timeline informing them when to begin and end each of the three data-collection periods. Each network log took approximately one minute to complete. Two incentives were offered to increase the completion rate of network logs. Attached to each log was a different AIDS Awareness Trading Card. Cards provided unique educational information about HIV , . .O‘F"'. “gt”; pp ,yopp‘e :.,t». oxgra: F4 ......,.....' Lillian..- .- ease of L?:.‘.v1d the in: n";' . a~5abj 52 infection and AIDS, as well as about individuals from infected and affected communities. Additionally, all programs from which individuals participated had a chance to win cash prizes determined by a lottery system. Five prizes of $100 each were given to selected HIV prevention programs. Individuals recorded several types of information about each communication interaction made during this time period, including the name of the individual with whom the HIV educator interacted, the individual's organization and organizational affiliation, the communication channel used for the interaction, the length of time spent interacting (or the number of pages if the interaction was a letter or facsimile), the extent to which the communication event was related to issues about HIV prevention programming, and the perceived importance of the interaction. This data helped determine interorganizational network . activity often measured by interaction frequency and network size (Papa & Tracy, 1988; Yum, 1983), and network diversity which refers to the degree of heterogeneity among members in an individual's network (Burt, 1983; Marsden, 1990; Rogers & Kincaid, 1981). The remaining information also provide network measures such as the relative richness of the communication channel used (Daft & Lengel, 1984), the perceived importance of the communication event, and the approximate percent of time spent communicating specifically about HIV programming. Each variable was measured continuously except organizational affiliation which was measured by six mutually exclusive categories. Participants were encouraged to complete the network logs after each communication contact, or minimally at several points throughout the ' ‘ :3". A o‘rr' ' It; «a .- ,. tcdk C r a 1" '. 'L.‘" i wise Luté on", v” CV‘I-u‘s xv: 5,: f - «V‘u V . V i...” ‘Esflu. Pr: question: 53 day, rather than at the end of the day since past research indicates that "people do not know, with any acceptable accuracy, to whom they talk over a given period of time" (Bernard, Killworth, & Sailer, 1981, p.15). 6. WWW During the final data-collection period, a self—administered written questionnaire was mailed to each participant that collected communication network data during the third and final data-collection period (Appendix E). This questionnaire asked educators to rate the relative importance and likelihood of use of a variety of activities associated with the Health Action Model (Figure 2, page 34). Educators were asked to imagine that they had been hired to begin a new HIV prevention education program. They were then asked to rate the relative importance of 20 activities related to the design, implementation, and evaluation of the program. They were also instructed not to select and rate a particular activity if it was not one they would likely engage in. The questionnaire took approximately 15 minutes to complete and was collected by the investigator along with the communication logs shortly after the last data-collection period ended. 7. WWW Prior to data collection, the quantitative demographics questionnaire, the communication network log, and the Health Action Pixel prograi :3 four HIV erloyees we Fisccnsin. :‘ifficulty. irstractiors per: ived w; 1136 . Data 5 deiertine i.- at least one has“ on th. ireziiltfilc'les taracted an de Strategiu 54 Model programming questionnaire were each pretested among 12 individuals in four HIV prevention programs in Michigan. These programs and their employees were similar to the types of programs and respondents in Wisconsin. Each data collection instrument was pretested for ambiguity, difficulty, the extent to which pretest participants were able to follow instructions, and specifically for the communication network logs, the perceived willingness to complete the logs over an extended period of time. C. Treatment of the Data Data gathered during the initial telephone interview were used to determine which HIV service providers in the state of Wisconsin offered at least one HIV prevention education program. This determination was based on the program administrator's responses to the questionnaire. Frequencies were computed (1) to determine the extent to which programs targeted audiences according to certain risk factors, and (2) to determine the extent to which different types of communication media and strategies were used in HIV prevention programming. Data obtained from administrators about HIV programs were typed into a WordPerfect 6.0 (1993) file. General program descriptions were used to verify the fact that the program focused on HIV prevention education, had been in existence for at least one year and had fewer than 10 paid staff members. Data pertaining to the geographic location of each program was used to determine the relative geographic proximity of each program. Data cognicati ntered int statistics ' Correlation castrated to tafferentia‘ iccczanicat the regress: :eil mean i: The ma C0Emioati mfgl'Poratit S(irrelation the inpact 1 “Penance mgr,“ p13. Firs-t social diff Shaping in: ”Sable, regs Wistent,‘ 55 Data obtained from the demographics questionnaire, the communication logs, and the Health Action Model programming survey were entered into a SPSS 6.0 (1994) file for analysis. Summary descriptive statistics were first computed for items in each instrument. Correlation coefficients and analyses of variance (ANOVA) were next computed to determine the impact of the independent variables (social differentiation and geographic proximity) on the dependent variables (communication network variables). To adjust for unequal cell sizes, the regression approach to analysis of variance was used, where each cell mean is given equal weight regardless of its sample size. The next step in the analysis was to determine the impact of the communication variables on the perceived importance and potential ' incorporation of aspects of the Health Action Model in program planning. Correlation coefficients and regression analyses were run to determine the impact of differential communication networks on the perceived importance and incorporation of Health Action Model characteristics in program planning. D. Limitations First, it is acknowledged that other variables, in addition to social differentiation and geographic proximity, may play a part in shaping interorganizational communication networks. To the extent possible, these variables were controlled. The amount of organizational resources and length of time in operation, for example, have both consistently been shown to impact the development of interorganizational CI“ . -1 av‘8’Y' ,}..‘e 0* . a. - .pbfi V"" Mikel Lu FLA. 9‘» VG: 1a. Auk erorg anomt < :ield S: 56 linkages, although the importance of the former has been established primarily in relation to formal linkages between organizations aimed at achieving specific goals. Variability in organizational resources was accounted for by establishing an upper limit on the number of paid staff a program may have. Any program with more than ten individuals was excluded. Similarly, a minimum length of time a program has been in operation was established since network development takes time. Any program operating for less than one year was excluded. Other variables that were not controlled, but thought to covary with the development of interorganizational communication networks, such as an individual's amount of experience in the health field or experience in the health field specifically related to HIV/AIDS, were measured and analyzed. Another limitation of the investigation concerns the use of logs to obtain information about the interorganizational communication networks of HIV educators. Previous studies have shown that individuals often underestimate interactions when utilizing this technique. Interactions are underreported because subjects typically do not make entries immediately following each communication event, and because subjects tend to omit short interactions initiated by another individual (Higgins, McClean, & Conrath, 1985). Participants tend to make periodic entries during the day, or in some cases wait until the end of the day to complete network logs. Even considering this weakness, however, this technique still remains the most pragmatic and realistic option, and one which has been used in several other studies (Conrath, Higgins, & McClean, 1983; Milardo, 1982; and Wheeler & Nezlek, 1977). Furthermore, this limitation was addressed in training by stressing to participants U- hart: this is Prevent "F t' ... he g“ 57 the importance of recording each interorganizational contact immediately after it occurs. A statistical limitation exists due to the fact that the total number of respondents was relatively small. As such, and with the statistical methods applied, some actual effects were unlikely to be found. For all analyses, the alpha was consistently set at the .05 level. Given this level and the number of respondents, only large effect sizes were likely to be found. Medium effect sizes may have been found, however, small effect sizes were unlikely to have been found. A final limitation concerns the Health Action Model. In general, this is a resource-intense planning model and yet in the area of HIV prevention resources are often scarce. Some of the study participants are affiliated with programs that have few resources and as such may not be as likely to incorporate as many aspects of the Health Action Model as will their resource-rich counterparts. N, arson Telep? identified a grograms. .1 identified 3 triori crite focused on E least one ya Sixty-five treinizatim The c immatior. FTC-seated i: 11‘. “.uStI'aIES “Mitre "“1- Malilated Data focused on. SpecifiCalL Situational I VH8 3180 a HIV p IV. RESULTS A. Program Identification and Description Survey Telephone calls were made to each of the 157 organizations identified as those most likely to administer HIV prevention education programs. After telephoning each organization, 39 organizations were identified with one or more HIV prevention programs fitting these a priori criteria (1) at least half of the program's activities were focused on HIV prevention, (2) the program had been in operation at least one year, and (3) the program had 10 or fewer paid employees. Sixty-five separate HIV prevention programs were identified by the 39 organizational administrators. The organizational type of each HIV prevention program is presented in Table 1. Organizational characterizations are based on information obtained from the CDC National AIDS Clearinghouse. Table 1 illustrates that the majority of the programs are embedded within community-based organizations, and that over three-fourths are affiliated with either community-based organizations or departments of public health. Data were also obtained about the target audience each program focused on. Program administrators were asked whether or not they specifically target individuals based on a list of 13 demographic, situational, and behavioral characteristics (Figure 4). Interviewees were also asked to list audience characteristics not found on the list. HIV prevention programs may segment their audience based on 58 59 Table 1. Organizational type of HIV prevention programs. Organizational Number of HIV Percent of HIV IXDE ev i s Egggggm§______ Community-based organization 353 53.8 Public health organization 15 23.1 Social service organization 6 9.2 Clinic 5 7.7 Professional organization 2 3.1 Educational organization 1 1.5 Other .1 l+i Total 65 100 W Simian]. Behaximl Gender Education level Hemophiliac Age Homelessness Injection drug use Ethnicity Socioeconomic status Other drug use Citizenship Primary language Prostitution Sexual orientation Figure 4. Characteristics of audiences targeted by prevention programs. Nine community-baSed organizations are also AIDS Service Organizations which provide HIV prevention programming to multi-county areas. 60 several of these characteristics, which would constitute a very narrowly defined audience, or based on none or one of these characteristics, which would constitute a very broad and general program. The average number of characteristics identified by program administrators was 2.2. Age was identified by program administrators the most frequently, followed by ethnicity, non-injection drug use, injection drug use, and socioeconomic status (Table 2). Adolescents were the age group most frequently targeted (27 of the 36 programs), while African Americans, Hispanics, and Native Americans respectively were the ethnicities targeted most frequently. B. Demographics Questionnaire After the telephone interviews, a written questionnaire was mailed to each HIV educator in each program. The purpose of this questionnaire was to determine the relative degree of social differentiation between HIV educators in the state of Wisconsin. Seventy-five surveys were mailed and 58 were returned providing a reaponse rate of 77 percent. Summary results are highlighted below. Many of the questions were demographic in orientation, while others focused on individuals' experience as educators, and perceived similarity to other educators. Of the 58 respondents, 72 percent were female, 72 percent had at least a college degree, 74 percent were heterosexual, and 80 percent were Caucasian. Educators ranged in age from 19 to 60, with an average age of 39. The average number of years program staff had been in health education was 7.4, while the average 61 Table 2. Program targeting characteristics. 30 25..- .................. 20~--- 15" a b c d e f g h i j k l m (a) Gender (h) Primary language (b) Age (i) Hemophiliac (c) Education level (j) Injection drug use (d) Ethnicity (k) Other drug use (e) Homelessness (l) Prostitution (f) Socioeconomic status (m) Sexual orientation 62 number of years they had been in HIV education specifically was just over 4 years. Eighty-seven percent of the HIV educators thought they were somewhat or very similar to other HIV educators, and 55 percent thought they were somewhat or very similar to other health educators, not necessarily in the area of HIV. Fifty—nine percent thought they had about the same amount of status as other HIV educators, and 80 percent said they do not socialize with other HIV educators. A social differentiation score was computed for each respondent by assigning a value to responses that differentiated them from other HIV educators. Table 3 illustrates how points were allocated for each item to determine a social differentiation score for each respondent. The maximum number of ”points" a respondent could have been assigned was 10 if all responses were completed. After totaling each respondent's score, the score was divided by the total number of items responded to and then multiplied by 10. This was done to account for missing data. The high score for any single respondent was 8.33 representing a high degree of social differentiation (an HIV educator who is very dissimilar to other HIV educators), and the low score for a single respondent was .50 representing a low degree of social differentiation (an HIV educator who is very similar to other HIV educators). Individuals' scores were ranked from high to low and divided into quartiles (Table 4). 63 Table 3. Determination of social differentiation scores. Response categories and percent Emaflns sijammmms Laumauumaman Gender Female 722 One point assigned for Male 282 "male" response Age Open-ended response category One point assigned for Range 19 - 60 responses in Quartile I Quartile I (19 - 31) or Quartile IV Quartile II & III (32 - 45) Quartile IV (46 - 60) Education Less than high school degree 12 One point assigned for High school degree 132 all responses except Associates degree 142 "college degree" College degree 542 Graduate degree 182 Sexual Heterosexual 742 One point assigned for orienta- Homosexual 182 "homosexual" or tion Bisexual 52 "bisexual" response Missing 32 Ethnicity Caucasian 802 One point assigned for African American 112 each response other than Hispanic 62 ”Caucasian" Native American 32 Years in Open-ended response category One-half point assigned health Range from less than 1 to 36 years for responses in education Quartile I (0 - 2) Quartile I and Quartile Quartile II & III ( 3 - 10) IV Quartile IV (11 - 36) Years in Open-ended response category One point assigned for HIV Range from less than 1 to 14 years responses in Quartile I education Quartile I (0 - 1) and Quartile IV Quartile II 5 III (2 - 6) Quartile IV (7 - 14) Similarity Very similar 112 One-half point assigned to other Somewhat similar 772 for "somewhat HIV Somowhat dissimilar 122 "dissimilar” response; educators Very dissimilar 12 one point assigned for ”very dissimilar" response. Table 3 (cont'd). 64 Similarity Very similar 122 One-quarter point to other Somewhat similar 432 assigned for "somewhat health Somewhat dissimilar 402 "dissimilar" response; educators Very dissimilar 52 one-half point assigned for "very dissimilar" response Relative More status 132 One point assigned for status Same status 602 for "more status" or level Less status 272 "less status" response Socialize Yes 192 One point assigned for with other No 812 "no" response HIV educators Table 4. Quartile division based on social differentiation scores. Number of m1: Scares Indixiduals High Low Range I 8.33 5.50 (2.83) 16 II 5.25 3.75 (1.50) 13 III 3.50 2.75 (.075) 14 IV 2.50 .50 (2.00) 15 65 2. W A geographic proximity score was computed for each program. ”Point" values were assigned to each program based on four criteria: (1) three "points” were allocated for each program from another organization located within a three mile radius, (2) two "points” were allocated for each program from another organization located within a 25 mile radius, (3) one ”point" was assigned for each program from another organization located in the same county, and (4) one "point" was assigned for each program within the same organization. Program scores for geographic proximity ranged from a high of 45 (geographically very close to other HIV prevention programs) to a low of zero (geographically very far from other programs). After scores were assigned to each program, programs were divided into quartiles from most to least geographically proximate (Table 5). Table 5. Quartile divisions based on geographic proximity scores. Number of manila Emma Indium: High Low Range I 45 35 (10) 17 II 34 24 (10) 14 III 7 3 (4) 10 IV 2 o (2) 11 58 66 3. ’ ' ' ell Individuals were assigned to cells within a 2 X 2 matrix based on social differentiation and geographic proximity scores and subsequent quartile ranking (Figure 5). Individuals were placed into each cell based on the extent to which they were most representative of the characteristics of that particular cell. Cell A (close and similar to others) Individuals were placed in Cell A if they were previously categorized in Quartiles I or II for geographic proximity and Quartiles III or IV for social differentiation. Ten individuals were placed into Cell A based on these criteria. Cell B (close and dissimilar to others) Individuals were placed in Cell B if scores were categorized in Quartiles I or II for geographic proximity and Quartiles I or II for social differentiation. Twenty-two individuals were placed into Cell B based on these criteria. Cell C (far and dissimilar to others) Individuals were placed in Cell C if they were categorized in Quartiles III or IV for geographic proximity and Quartiles I or II for 67 social differentiation. Seven individuals were placed into Cell C based on these criteria. Cell D (far and similar to others) Individuals were placed into Cell D if they were categorized in Quartiles III or IV for geographic proximity and Quartiles III or IV for social differentiation. Nineteen individuals were placed into Cell D based on these criteria. MW Close Far A) Close D) Far Similar and similar and similar Social (n - 10) (n - l9) Differentiation B) Close C) Far Dissimilar and dissimilar and dissimilar (n - 22) (n-7) Figure 5. Educator placement grid. 68 C. Communication Network Logs Of the 37 HIV educators that originally agreed to participate, 33 completed one or more communication logs. Individuals that did not complete any communication logs were dropped from the study. A total of 985 communication logs were returned, however 35 were excluded that were improperly completed leaving a total of 950 useable communication logs. Of these, 36 percent were completed during the first three-week data collection period, 35 percent were completed during the second three- week data collection period, and 29 percent were completed during the final data collection period. Summary statistics for the communication logs are provided below (Table 6). Here, data is analyzed at the interaction level of analysis. 1. W Cell Respondents in Cell D had the largest percentage of interactions (352) followed by respondents in Cell B (322), Cell C (182), and finally Cell A (142). When the mean number of contacts per individual in each cell is considered, however, these results change significantly. The average number of contacts per respondent in Cell 8 is highest with 38, followed by 35 in Cell C, 34 in Cell A, and 21 in Cell D. 69 Table 6. Communication log summary findings. Mable W Cell Cell A -- Proximate and similar 142 Cell 8 -- Proximate and dissimilar 322 Cell C -- Not proximate and dissimilar 182 Cell D -- Not proximate and similar 352 Organizational type AIDS Service Organization 342 Community-based organization 292 Public health organization 122 Social service organization 162 Clinic 92 Educational organization 12 Affiliation type Local HIV/AIDS organization 162 Non-local HIV/AIDS organization 132 Local non-HIV/AIDS health organization 172 Non-local non-HIV/AIDS health org. 62 Local non-health organization 322 Non-local non-health organization 142 Communication channel Face-to-face 452 Telephone 482 Facsimile / letter 62 Minutes or pages Open-ended response category Mean - 29.68 of the interaction Percent of the 25 percent 82 interaction related 50 percent 122 to HIV programming 75 percent 182 issues 100 percent 622 Minutes or pages of the interaction related to HIV programming Importance of the interaction Obtained by multiplying the number of minutes or pages of the inter- action by the percent of the interaction related to HIV programming Rated on a scale of 0 (not important) to 10 (extremely important) Mean - 23.38 Mean - 6.82 70 Organizational Type Individuals employed by AIDS Service Organizations (ASO) accounted for the largest percentage of interactions. Each AIDS Service Organization is also a community-based organization. They are separated here because ASOs are unique in that each is responsible for providing education to multiple counties situated within a specific geographic area. Community—based organizations accounted for another 29 percent of the interactions followed by social service organizations (16 percent), public health organizations (12 percent), clinics (9 percent), and educational organizations (1 percent). Affiliation Type Respondents indicated on the communication log with whom, if anyone, the interactant was affiliated. Interactants were first differentiated by physical distance (local versus non-local), and then by organization type. Organizations were identified either as an HIV/AIDS service provider, a health service provider not focusing primarily on HIV/AIDS, or an organization not related to health service provision (individuals not affiliated with any organization also fell into this last category). Most interactions (65 percent) were with individuals characterized as local contacts. Twenty-nine percent of the interactions were with individuals employed by HIV/AIDS Service Organizations, and another 23 percent were with individuals employed by organizations that provide health related services not specifically 71 focusing on HIV/AIDS. Communication Channel Nearly all interactions (93 percent) occurred either in person or over the telephone. The remainder of the interactions were letters or facsimile transmissions. Interactions that occurred either in person through face-to-face contact or over the telephone were analyzed separately from interactions that occurred through facsimiles and letters. These different types of communication channels were separated for analysis because (1) there was such a large discrepancy in the number of each interaction type (93 percent versus seven percent) and (2) because the length of the interactions were recorded using different measures. Number of minutes of the interaction was used to record face- to-face and telephone interactions, while number of pages was used to record facsimiles and letter interactions. Minutes or Pages of the Interaction Participants were instructed to document the number of minutes or pages of each interaction. The mean for this open-ended response category was about 30. Broken down by communication channel, the average number of minutes per face-to-face or telephone interaction was nearly 31, while the average number of pages per letter or facsimile interaction was approximately 7. 72 Percent of the Interaction Related to Programming Individuals estimated the amount of each interaction specifically related to HIV prevention education using 25 percent, 50 percent, 75 percent, or 100 percent as response categories. Participants' responses indicated that a majority of the interactions (62 percent) were 100 percent related to HIV programming. Eighteen percent of interactions were thought to be three-fourths related to programming, 12 percent one- half related, and eight percent one-quarter related to programming. Overall 83 percent of the content of each face-to-face and telephone interaction was reported to be related specifically to HIV programming, whereas 96 percent of the content of each letter or facsimile interaction was reported to be specifically related to HIV programming. Hinutes or Pages of the Interaction Related to Programming This figure was calculated by multiplying the number of minutes or pages of the interaction by the percent of the interaction related to programming. This computation provides a measure of the actual amount (minutes or pages) of each interaction related to HIV programming. The mean number of minutes or pages of each interaction related to HIV programming was 23.4. When specific communication channels were considered, the average number of minutes related to HIV programming was about 24 for face-to-face and telephone interactions, and the average number of pages related to HIV programming was about 7 for letters and facsimiles. 73 Importance of the Interaction The relative importance of each interaction was the last piece of information participants recorded for each interaction. Individuals were asked to rate each interaction on a scale of 0 (not important) to 10 (extremely important). The mean for all interactions was 6.82. This figure differed little by communication medium. Mean Scores by Cell and Channel Type Mean scores for the last six variables are presented for both face-to-face and telephone interactions (Table 7) and for interactions occurring through letters and facsimiles (Table 8). Mean scores are shown both for all respondents and for respondents unaffiliated with an . AIDS Service Organization. Mean scores are further differentiated according to participants' placement within cells. Consider some face- to-face and telephone variables first. Scores for the variable channel were computed by assigning a "one” to each face-to-face interaction and a ”two" to each telephone interaction. A mean score of 1.5, as is the case for Cell D, indicates an equal number of interactions for each communication medium. Scores under 1.5, such as those for Cells A and B, indicate a somewhat greater reliance on face-to-face interactions rather than telephone interactions. The larger mean score (1.8) for Cell C denotes a greater reliance on telephone interactions than face- to-face interactions. This difference is not apparent, however, when responses from ASOs are excluded from analysis. In fact, the mean 74 Table 7. Mean scores for face-to-face and telephone interactions. Face-to-face and telephone interactions (all responses) Mean scores and (standard deviations) Affiliatien Chemel Minutes Cell A.(n - 130) 3.8 (1.8) 1.4 (.5) 47.3 (54.5) B (n - 282) 3.0 (1.6) 1.4 (.5) 41.1 (45.1) C (n - 158) 4.1 (1.6) 1.8 (.4) 9.4 (18.7) D (n - 311) 4.0 (1.7) 1.5 (.5) 25.8 (31.0) Face-to-face and telephone interactions (all responses) Mean scores and (standard deviations) Percent Minutes HIV HIV Impor- related related tense. 5191.]. A (In - 130) 3.1 (1.1) 32.5 (41.8) 7.1 (2.2) B (n - 282) 3.5 (1.0) 35.7 (41.3) 7.1 (2.9) C (n - 158) 3.5 (1.0) 6.5 (9.5) 6.4 (2.3) D (h - 311) 3.2 (.9) 20.2 (24.3) 6.7 (2.2) gel]. A: Close and similar to other HIV educators Cell B: Close and dissimilar to other HIV educators all C: Far and dissimilar to other HIV educators D: Far and similar to other HIV educators Table 7 (cont'd). A (n B (n C (n D (n A (n B (n C (n D (n 122) 265) 27) 164) 122) 265) 27) 164) 75 Face-to-face and telephone interactions (excluding ASO responses) Mean scores and (standard deviations) Affiliatien 3.8 3.0 4.0 3.8 (1.8) (1.6) (1.5) (1.7) Channel 1.4 1.4 1.3 1.5 (.5) (.5) (.5) (.5) tes 49.0 (55.3) 39.9 (43.9) 29.4 (38.2) 25.9 (30.8) Face-to-face and telephone interactions (excluding ASO responses) Mean scores and (standard deviations) Percent HIV related 3.1 3.5 2.7 3.5 (1.1) (1.0) (1.3) (.8) Minutes HIV related 33.7 35.0 15.9 21.9 (42.6) (40.9) (17.9) (26.4) Impor- 7.1 (2.2) 7.1 (3.0) 6.7 (2.5) 7.4 (2.3) Table 8. A (n B (n C (n D (n A (n B (n C (n D (n Cell Cell Cell Cell unm> 6) 18) 15) 22) 6) 18) 15) 22) 76 Mean scores for facsimile and letter interactions. Facsimile and letter interactions (all responses) Affiliatien Channel 2.2 (1.9) 3.0 (0) 3.6 (1.6) 3.0 (O) 3.9 (1.7) 3.0 (O) 3.6 (1.8) 3.0 (0) Facsimile and letter interactions Percent HIV 1 related 4.0 (0) (0) (.8) (.7) 4.0 3.7 3.7 (all responses) Pages HIV related 3.0 4.9 3.1 12.2 (20.2) (-6) (8.2) (2.7) ° Close and similar to other HIV educators Close and dissimilar to other HIV educators ° Far and dissimilar to other HIV educators Far and similar to other HIV educators 3.0 (.6) 4.9 (8.2) 3.7 (3.4) 12.5 (20.1) Impor- (2.8) (3.2) (1.5) (2.6) 77 Table 8 (cont'd). Facsimile and letter interactions (excluding ASO responses) Affiliatien Channel Pages Cell A (n - 6) 2.2 (1.9) 3.0 (0) 3.0 (.6) B (n - 18) 3.6 (1.6) 3.0 (O) 4.9 (8.2) C (n - 0) 0 (n/a) 0 (n/a) 0 (n/a) D (n - 19) 3.2 (1.7) 3.0 (0) 12.9 (21.0) Facsimile and letter interactions (excluding ASO responses) Percent Pages HIV HIV Impor- relatesi related tanee_ Cell A (n - 6) 4.0 (O) 3.0 (.6) 7.0 (2.8) B (n - 18) 4.0 (O) 4.9 (8.2) 5.6 (3.2) C (n - 0) 0 (n/a) 0 (n/a) 0 (n/a) D (n - 19) 3.6 (.8) 12.6 (21.2) 8.8 (1.9) 78 score for Cell C in the latter is lower than mean scores for respondents in Cells A, B, and D. The average number of minutes per interaction is considerably different for Cells A and B than it is for Cells C and D. The mean number of minutes per interaction is 47.3 and 41.1 respectively for Cells A and B, whereas the mean number of minutes for Cells C and D is 9.4 and 25.8 respectively. A similar result is found for the number of minutes of each interaction related to HIV programming. Here the average number of minutes for Cells A and B is 32.5 and 35.7 and the average number of minutes for Cells C and D is 6.5 and 20.2. Table 8 which provides scores for facsimile and letter interactions also illustrates some interesting differences between cells. Affiliation type is a relative measure of the extent to which the interactant may provide useful information to the HIV educator. Lower numbers (”one“ and 'two”) were assigned to individuals affiliated with local and non-local HIV/AIDS organizations, mid-range numbers ("three" and "four") were assigned to individuals affiliated with local and non-local health organizations not focusing primarily on HIV, and higher numbers ("five" and ”six') were assigned to individuals either affiliated with local and non-local organizations unrelated to health or individuals not affiliated with any organization. Individuals in Cell A had a mean score for the interactants of 2.2 (relatively better sources of information) whereas individuals in the other three cells had scores of 3.6 or higher. Individuals in Cell D received facsimiles and letters averaging over 12 pages in length, while individuals in the other cells had averages ranging from about 3 to 5, a result that remained constant 79 for interaction amount specifically related to HIV programming. Results differ only slightly when respondents affiliated with an AIDS Service Organization were excluded from analysis. Mean scores for Cell A and B remain unchanged, however, there are no interactions in Cell C. Results for respondents in Cell D remain consistent, except for the variable importance which was rated higher than for all respondents (M - 8.8 versus M - 8.1). Mean scores at the individual (respondent) level of analysis were also computed for five network variables including total contacts, unique contacts, unique affiliations, total minutes, and total minutes specifically related to HIV programming. Results are presented below (Table 9). Total Contacts Total contacts represents the average total number of communication network logs completed by each individual. Respondents in three cells had an average number of total contacts in the mid-305. Only respondents in Cell D differed substantially with just over 20 total contacts per person. When responses from individuals affiliated with ASOs are excluded, however, mean scores for respondents in Cell D are similar to mean scores for respondents in Cell C (approximately 14), and the mean scores for respondents in Cells A and B are similar, but higher than with all respondents considered (approximately 43 and 48 respectively). 80 Table 9. Communication log individual level mean scores. All respondents (n - 33) Mean scores and (standard deviations) Unique Total Unique affilia- mntaeta eentacta tiena Cell A (n - 4) 34.0 (29.2) 28.0 (28.1) 24.3 (24.8) B (n - 8) 38.4 (27.4) 26.8 (20.9) 20.3 (16.3) C (n - 5) 34.6 (26.6) 29.2 (25.4) 23.8 (20.9) D (n - 16) 20.9 (20.6) 14.4 (15.3) 11.6 (11.9) All respondents (n - 33) Mean scores and (standard deviations) Total minutes Total HIV minutes related Cell A (n - 4) 1525.5 (1403.1) 1086.3 (919.5) B (n - 8) 1449.5 (1321.7) 1267.3 (1242.4) C (n - 5) 297.8 (320.3) 234.0 (225.1) D (n - 16) 500.3 (568.1) 405.3 (468.5) Cell A: Close and similar to other HIV educators Cell 8: Close and dissimilar to other HIV educators Cell C: Far and dissimilar to other HIV educators Cell D: Far and similar to other HIV educators 81 Table 9 (cont'd). A (n B (n C (n D (n A (n B (n C (n D (n Respondents excluding ASOs (n — 24) Mean scores and (standard deviations) Unique Total Unique affilia- s‘entaeta eentaeta tiena 3) 42.7 (28.7) 34.7 (30.2) 30.0 (26.9) 6) 48.2 (24.0) 32.8 (20.6) 24.7 (16.6) 2) 13.5 (17.7) 10.0 (12.7) 8.0 (9.9) 13) 14.2 (11.2) 9.8 (10.6) 7.8 (6.1) Respondents excluding ASOs (n - 24) Mean scores and (standard deviations) Total minutes Total HIV minutes related 3) 1975.7 (1317.9) 1410.0 (799.6) 6) 1764.2 (1389.0) 1546.0 (1326.3) 2) 397.5 (555.1) 278.0 (391.7) 13) 324.5 (462.0) 297.7 (410.7) 82 Unique Contacts The average number of unique contacts represents the number of different individuals a participant interacted with during the data collection period. Respondents in Cells A, B, and C had approximately twice the number of unique contacts than did respondents in Cell D. Individuals in Cells A, B, and C interacted with approximately 28 different individuals each, while individuals in Cell D interacted with only about 14 different individuals each. When respondents affiliated with ASOs were excluded from analysis, mean scores for respondents in Cells C and D again were very similar (approximate 10) as were those for Cells A and B (35 and 33 respectively). Unique Affiliations Unique affiliations represent the average number of different organizations each respondent interacted with. An interactant who was not affiliated with an organization was still considered a unique 3 affiliation. Respondents in Cells A, B, and C each interacted with individuals representing over 20 unique affiliations. Respondents in Cell D had a lower number of unique affiliation interactions, however, averaging about 12. This difference is eliminated when responses from ASOs are excluded. Further, mean scores for Cells C and D are similar and substantially less (approximately 8) than the mean scores for respondents in Cells A and B (30 and 25 respectively). 83 Total Minutes The number of total minutes represents the total average number of minutes respondents interacted with others through face-to-face or telephone communication channels. Participants in Cells A and B interacted much longer than did participants in Cells C and D. The mean total number of minutes for individuals in Cells A and B are 1525.5 and 1449.5 respectively, while the mean total number of minutes for individuals in Cells C and D are 297.8 and 500.3 respectively. When the responses from ASOs are excluded, the total number of minutes for respondents in Cells C and D is closer (approximately 325 and 398 respectively). The difference in mean scores between respondents in Cells A and B and respondents in Cells C and D is even larger than the difference when all respondents were considered in the analysis. Total Minutes Program Related This figure represents the average number of minutes respondents spent interacting with others specifically about HIV programming. Results here are similar to those found for total minutes with individuals in Cells A and B interacting much more (1086.3 and 1267.3 respectively) than individuals in Cells C and D (234 and 405.3 respectively). Results excluding individuals affiliated with AIDS Service Organizations are similar here as they have been for the other variables, where mean scores for respondents in Cells C and D are very 84 similar (approximately 280) and considerably less than means scores for respondents in Cells A and B (1410 and 1546 respectively). Analrais.) The correlation coefficients presented here treat each interaction (that is, each network log completed; n - 950) as the unit of analysis and concern (1) associations between social differentiation and communication network variables, (2) associations between geographic proximity and communication network variables, and (3) associations between communication network variables only. For social differentiation and geographic proximity, correlations were computed both for raw scores and for relative rank scores‘. Since the results were very similar, correlation scores (”r”) and significance values ('p") are shown in Tables 10 through 12 only for raw scores. Significant correlations found using rank scores, but not raw scores are discussed. Additionally, Tables 10 through 12 illustrate separate correlations for (1) all participants and (2) for all participants unaffiliated with AIDS Service Organizations. Finally, correlations are Raw scores for social differentiation were computed so that higher scores represent less similarity with other HIV educators, while rank scores for social differentiation were computed so that lower scores represent less similarity with other HIV educators. Raw scores for geographic proximity were computed so that higher scores represent individuals who are geographically closer to other HIV educators, while rank scores for geographic proximity were computed so that lower scores represent individuals who are geographically closer to other HIV educators. 85 Table 10. Social differentiation correlations (interaction level). SOCIAL DIFFERENTIATION Minutes or Affili- Minutes pages HIV Impor- anon—ChanneleLeasesrelated—tanee. (All participants) All channels r - -.12“‘ r - .08' --- --- --- Face-to-face & telephone --- --- r - -.07' n/s n/s Facsimile & letter --- --- n/s n/s r - -.50"' (Excluding ASOs) All channels r - -.18“' n/s --- --- --- Face-to-face & telephone --- --- n/s n/s r - -.11“ Facsimile & letter --- --- n/s n/s r - -.60“' (*) p < .05; (**) p < -01; (***) p < .001; (n/s) not significant (p > .05); (---) no correlation coefficient computed 86 Table 11. Geographic proximity correlations (interaction level). GEOGRAPHIC PROXIMITY Minutes or Affili- Minutes pages HIV Impor— atien__ Channel W m m (All participants) All channels r - -.22"‘ r - -.14”' --- --- --- Face-to-face & telephone --- --- r - .23'“ - .22'“ r -= .13‘“ Facsimile & letter --- --- n/s n/s n/s (Excluding ASOs) All channels r - -.16"' n/s --- --- --- Face-to-face & telephone --- --- r - .11“' r - .09‘ n/s Facsimile & letter --- --- n/s n/s r - -.34' (*) p3< .05; (**) p < .01; (***) p4< .001; (n/s) not significant (p‘> .05); computed (---) no correlation coefficient 87 Table 12. Network variable correlations (interaction level). Affili- Minutes Q£_Ré32§ (All participants) Variable: Importance All channels r - -.10“ r - -.10“ --- Face-to-face & telephone --- --- r _ .23». Facsimile & letter --- --- n/s Variable: Minutes Face-to-face & telephone n/s r - -.54“' --- Variable: Minutes related to HIV programming Face-to-face & telephone n/s r - -,49"‘ --- (*) p < .05; (**) p < .01; (***) p < -001; (n/s) not significant (p > .05); computed Minutes or pages HIV relateL. n/s (---) no correlation coefficient 88 provided for both face-to-face and telephone interactions, as well as for interactions occurring through facsimiles and letters. Interactant Affiliation Participants in the study interacted with a variety of individuals, some affiliated with an HIV/AIDS organization, some affiliated with a health service organization not specifically focusing on HIV/AIDS, and some either affiliated with an organization unrelated to health service provision or not affiliated with an organization at all. Individuals affiliated with certain of these organizations are considered to be better sources of information related to HIV prevention programming efforts. From potentially better sources of information, they are individuals affiliated with (1) local HIV/AIDS service organizations, (2) non-local HIV/AIDS service organizations, (3) local health service organizations, (4) non-local health service organizations, (5) local non-health related organizations, (6) non-local non-health related organizations. Those considered to be relatively better sources of information were assigned lower scores, while those considered to be relatively poorer sources of information were assigned higher scores. Results show significant correlations between interactant affiliation and social differentiation, geographic proximity, and importance. Specifically, relatively dissimilar HIV educators interacted with relatively better sources of information (all participants: [1 (929) - -.12, p < .001]; excluding ASOs: 89 [1 (614) - -.18, p < .001]). Additionally, individuals who are geographically closer to other HIV educators interacted with relatively better sources of information (all participants: [1 (929) - -.22, p < .001]; excluding A805: [1 (614) - -.16, p < .001]). Finally, those interactions rated as being relatively more important to the respondents were associated with better sources of information (all participants: [L (923) - -.10, p < .01]). Channel Participants interacted with individuals about HIV programming through a variety of communication channels. Some channels are considered richer than others (Daft & Lengel, 1984). Face-to-face interactions, for example, are considered the richest channels because feedback is immediate and nonverbal communication signs are incorporated into the interpretation of the message. Telephone interactions are less rich than face-to-face interactions because nonverbal signs are excluded from possible interpretation. Telephone interactions are still considered fairly rich though because they too allow for immediate feedback. The least rich of the communication channels considered here are letters or facsimile transmissions. These types of interactions lack nonverbal signs and lack immediate feedback. Relatively richer channels of communication were assigned lower scores, while relatively less rich channels were assigned higher scores. There are several significant correlations concerning the network variable channel. First, there is a significant association between 90 individuals who are relatively more similar to other HIV educators and the use of richer channels of communication (all participants: [1 (942) - .08, p < .05]). Individuals who are geographically closer to other HIV educators also use richer types of communication (all participants: [1 (942) - -.14, p < .001]). Interactions that were rated as relatively more important by participants were significantly associated with richer communication channels (all participants: [1 (937) - -.10, p < .01]). Finally, of the face-to-face and telephone interactions, those longer in duration were significantly correlated with richer types of communication (all participants: [1 (879) - -.54, p < .001]), and those that had more minutes related specifically to HIV programming were significantly correlated with richer communication channels (all participants: [1 (877) - -.49, p < .001]). Minutes or Pages of the Interaction This variable represents the total number of minutes of the interaction for face-to-face and telephone interactions or the total number of pages of the interaction for letters and facsimiles. Results show a significant association between relatively similar HIV educators and greater interaction through face-to-face and telephone interactions (all participants: [1 (879) - -.07, p < .05]). A significant correlation was also found between geographic proximity and interaction length. Specifically, being geographically close to other HIV educators ‘Uas associated with longer face-to-face and telephone interactions (all lNarticipants: [z (879) — .23, p < .001]; excluding ASOs: [I (576) - .11, 91 p < .01]). Finally, results point to a significant correlation between interaction length and importance. Specifically, longer face-to-face and telephone interactions were rated as being more important than shorter interactions (all participants: [1 (874) = .23, p < .001]). 0 Percent of the Interaction Related to Programming Participants were asked to estimate the extent of each interaction that was directly related to HIV programming. This variable was primarily used for computational purposes to determine the actual number of minutes or pages of each interaction that respondents spent "on task," or related specifically to HIV programming. As such this variable is not included in Tables 10 through 12. Nonetheless, there were some significant correlations associated with this variable that should be noted. First, a significant correlation was found between social differentiation and percent of the interaction related to HIV programming“. Specifically, more dissimilar individuals spent more time ”on task” during face-to-face and telephone interactions than did relatively more similar HIV educators [z (879) - -.08, p < .05]. There was also a significant relationship found between interaction importance and the extent to which the interaction was related to HIV programming, such that the more the interaction was related to HIV programming the more important it was considered to be [r (875) - .32, p < .001]. This is one instance where the significant correlation was found only with social differentiation rank scores. 92 Minutes or Pages of the Interaction Related to HIV Programming Participants indicated on the communication logs the approximate percent of the interaction that was related to HIV programming. This percentage was then multiplied by the actual number of minutes or pages of the interaction. The product represents the actual number of minutes or pages of the interaction related to HIV programming. Two significant correlations were found for this variable. First, participants who were relatively more proximate, spent more time per face-to-face and telephone interaction communicating about HIV programming than those who are geographically further (all participants: [1 (877) - .22, p < .001]; excluding A803: [1 (574) - .09, p < .05]). Also, participants rated longer face-to-face and telephone interactions related to HIV as being relatively more important (all participants: [1 (873) - .33, p < .001]). Importance of the Interaction Participants rated the relative importance of each interaction -from a low of "0" (not important) to a high of ”10” (extremely ' iummrtant). There were several significant correlations found for this ‘Variable, some which have already been noted. First, relatively more <1issimi1ar individuals perceived both face-to-face and telephone itnteractions (excluding ASOs: [; (573) - -.11, p < .01]) and facsimiles and letters (all participants: [1 (61) - -.50, p < .001]; excluding lkSOs: [z (43) - -.60, p,< .001]) as less important than did individuals ‘flho are relatively more similar. Further, individuals who were 93 geographically closer rated face-to-face and telephone interactions as more important than individuals who were geographically further (all participants: [1 (876) - .13, p < .001]). Just the opposite was found for facsimiles and letters. Individuals that were geographically closer rated facsimiles and letters as being less important than those who were geographically further (excluding ASOs: [I (43) - -.34, p < .05]). This last finding pertained to participants unaffiliated with an AIDS Service Organization. It should also be noted that a significant correlation ([1 (43) - .37, p < .01]) was found for all participants as well, however, only when the correlation was with the geographic proximity rank as opposed to the geographic proximity score. The next set of correlation coefficients treats each individual (n - 33), as opposed to each interaction, as the unit of analysis. The following correlations illustrate (1) associations between social differentiation and communication network variables, and (2) associations between geographic proximity and communication network variables. For social differentiation and geographic proximity, correlations were computed both for raw scores and for relative rank scores. Since significant results were identical, correlation scores ("r”) and significance values (”p”) are shown only for raw scores (Tables 13 and 14). Again, separate correlations are illustrated for (1) all participants (n - 33) and (2) for all participants unaffiliated 94 Table 13. Social differentiation correlations (individual level). Unique Total Unique affilia- Total eentaetaaentactatiene—minutee (All participants) n/s n/s n/s n/s (Excluding ASOs) n/s n/s n/s n/s Minutes Total Average HIV Total facsimile minutes related faeflmileamaea— (All participants) n/s n/s n/s n/s (Excluding ASOs) n/s n/s n/s n/s Percent Average Affil- HIV innertaneeiatian Channel related (All participants) n/s n/s n/s n/s (Excluding ASOs) , n/s n/s n/s n/s (*) p < .05; (**) p < .01; (***) p < .001; (n/s) not significant (p > .05) 95 Table 14. Geographic proximity correlations (individual level). Unique Total Unique affilia- eentatta eentaeta titans.— (All participants) n/s n/s n/s (Excluding ASOs) r - .66'“ r - .61“ r - .62'“ Minutes Average HIV Total minutes related facsimiles (All participants) r - .38‘ r - .43' n/s (Excluding ASOs) n/s r - .55“ n/s Average Affil- imnartanse iatian Channel (All participants) n/s n/s n/s (Excluding ASOs) n/s n/s n/s (*) p < .05; (a) p < .01; (m) p < .001; (n/s) not significant (p > .05) Total I. - .45.. r - .58“ Total facsimile Rflgfii____ n/s Percent HIV related n/s n/s 96 with an AIDS Service Organization (n a 24). Total Contacts This variable represents the total number of interactions for each participant over the data-collection period. The only significant correlation found here concerns geographic proximity. Specifically, a strong positive association was found between HIV educators who were relatively closer geographically and the total number of contacts they had (excluding ASOs: [1 (24) - .66, p < .001]). This correlation was only significant when participants associated with AIDS Service Organizations were excluded from analysis. Unique Contacts The number of unique contacts represents the number of differene individuals each participant interacted with during the course of data <=ollection. Results show a significant correlation with geographic ‘Proximity such that relatively closer individuals interacted with a .greater number of different individuals (excluding A505: [1 (24) - .61, :n,< .01]). As was the case with total contacts, a significant Correlation was found only when individuals associated with AIDS Service Organizations were excluded from analysis. 97 Unique Affiliations This variable represents the number of diffezenf organizations represented by the unique contacts. Again, a strong correlation was found for geographic proximity (excluding A803: [1 (24) = .62, p < .001]). Geographically closer individuals unaffiliated with an AIDS Service Organization were associated with more unique affiliations. Total Minutes The total number of minutes each participant interacted through face-to-face and telephone interactions was significantly correlated with geographic proximity. Specifically, a positive association was found between physically closer respondents and a greater number of total minutes spent interacting with others. Here, a significant correlation was found when all participants were considered in the analysis [I (33) - .45, p < .01], as well as when individuals associated with AIDS Service Organizations were excluded from the analysis [I (24) - .58, p < .01]. Average Minutes This variable represents the average length (in minutes) of each participant's face-to-face and telephone interactions. A single positive correlation was found for geographic proximity, where physically closer individuals were associated with longer interactions 98 (all participants: [1 (33) - .38,-p < .05]). Minutes of Interaction Related to HIV Programming The number of minutes of face-to-face and telephone interactions actually spent ”on task” or related to HIV programs was computed by multiplying the total number of minutes of the interaction by the percentage of the interaction related to HIV programming. A significant correlation was found for geographic proximity such that physically closer individuals spent more time "on task" than did individuals who were further away (all participants: [; (33) - .43, p < .05]; excluding A808; [1; (24) - .55, n < .01]). Other Communication Network Variables Several other variables were correlated with social differentiation and geographic proximity at the individual level of analysis, however no significant relationships were found for either social differentiation or geographic proximity. The variables considered were (1) total number of facsimiles and letters, (2) total number of pages of facsimiles and letters, (3) average importance of the interactions, (4) affiliation type of the interactants, (5) communication channels, and (6) the percentage of the interaction specifically related to HIV programming. 99 Years of HIV Educator Experience Two variables associated with HIV educators, years of experience in the field of health and years of experience specifically in the area of HIV/AIDS, were correlated with each of the following network variables (1) total contacts, (2) unique contacts, (3) unique affiliations, (4) total minutes spent interacting, (5) average minutes spent per interaction, (6) minutes spent interacting about HIV programming, and (7) interactant affiliation type. These correlations were run to determine the impact of experience on network variables. There were no significant correlations for the variable considering the number of years of experience in the field of health. There were three significant correlation coefficients though between the number of years in the area of HIV/AIDS and (1) total contacts [1 (33) - -.36, p < .05], (2) unique contacts [1 (33) - -.39, p < .05], and (3) unique affiliations [f (33) - -.38, p < .05]. Interestingly, each of the significant coefficients was negatively related to the network variables indicating an association between less HIV educator experience and, in general, more contacts. Since only these few significant correlations existed and because they were in the opposite direction as was expected, other analyses did not consider years of experience as a covariate influence on interorganizational communication networks. 100 4. AnalxaiLeLilatianee An analysis of variance (ANOVA) was calculated for selected network variables thought to be most representative of relatively more and relatively less developed interorganizational communication networks. 'The enelyeie_ef_yefienee is a statistical technique that examines the variability of observations within each group (cell) as well as the variability across each group (cell). An analysis of variance was run for (1) total contacts, (2) unique contacts, (3) unique affiliations, (4) total minutes, (5) average minutes, (6) minutes related to HIV programming, and (7) affiliation type of the interactant. The analyses of variance were calculated based upon the specific cell in which each participant was placed. As such, the analyses are based on a 2 X 2 matrix characterized by low/high social differentiation (similar/dissimilar) and high/low geographic proximity (close/far). Again, separate analyses were run for all participants (n - 33) and for all participants excluding those employed by AIDS Service Organizations (n - 24). Total Contacts For the number of total contacts, a main effect was found for geographic proximity [E (1,24) - 11.61, p < .01] when individuals affiliated with AIDS Service Organizations were excluded from the analysis. The term me1n_effee§ is used to describe the impact of a single independent variable. There was no statistically significant 101 main effect for social differentiation and there was no significant interaction effect between social differentiation and geographic proximity. A large difference was found when all participants were included in the analysis and when those participants unaffiliated with an ASO were included in the analysis. The amount of variance explained in the former (R-square) is about 10 percent, while the latter is able to account for nearly 50 percent of the variance. Unique Contacts A main effect was found for geographic proximity [E (1,24) = 7.71, p < .05] when the analysis excluded individuals employed by AIDS Service Organizations. The amount of variance explained by this effect is 36 percent. There was no significant main effect for social . differentiation and no significant interaction effect between social differentiation and geographic proximity. Unique Affiliations Results for this variable were similar to those found for total contacts and unique contacts. That is, a significant main effect was found for geographic proximity [E (1,24) - 8.34, p < .01] only when participants affiliated with AIDS Service Organizations were excluded from the analysis. The amount of variance explained when ASOs were excluded was 37 percent. There was no main effect for social differentiation and no interaction effect between social differentiation 102 and geographic proximity. Total Minutes Findings from the analysis of variance for total minutes are similar. That is, a main effect was once again found for geographic proximity, however there was no main effect for social differentiation and no interaction effect between the two. Here, though, significant main effects were found both for all participants [E (1,33) - 9.22, p < .01] and_when members of AIDS Service Organizations were excluded from analysis [E (1,24) - 10.58, p < .01]. While a significant main effect was obtained each time, the model that excludes AIDS Service Organizations explains more variance (44 percent) than the model that includes participants from all organizations (26 percent). Average Minutes A significant main effect was identified for geographic proximity for all participants [E (1,33) - 10.79, p,< .01] and when individuals from AIDS Service Organizations were excluded [E (1,24) - 5.26, p < .05] from consideration. Here, a larger effect was found when all participants were included in the analysis. There was no significant nmin.effect for social differentiation and no significant interaction ‘between social differentiation and geographic proximity. Approximately 25 percent of the variance was explained for each group. 103 Minutes Related to HIV Programming Analysis of variance results again point to a significant main effect for geographic proximity for all participants [E (1,33) - 7.94, p < .01] and excluding ASOs [E (1,24) - 8.72, p < .01]. No significant main effect was found for social differentiation nor was any significant interaction effect noted. Forty-one percent of the variance was explained when AIDS Service Organizations were excluded, and 25 percent of the variance was explained when all respondents were included in the analysis. Affiliation Type of the Interactant For this particular variable, the analysis of variance did not reveal any significant main effects nor any significant interaction effects between social differentiation and geographic proximity. D. Programming Survey The purpose of this final survey was to determine (1) the perceived importance and extent to which HIV educators might incorporate aspects of the Health Action Model into HIV programming efforts, and (2) the impact of relatively more or less established communication networks on the potential use of Health Action Model aspects. Of the 33 participants who completed communication network logs, 32 also completed the Health Action Model programming survey representing a 97 percent 104 response rate. Respondents were asked to indicate the relative importance of 20 Health Action Model activities assuming they were to begin a new HIV prevention education program. They were further instructed not to rate those activities that they did not consider important and would not likely engage in. The score assigned to each item was the relative rating given by respondents. Items left blank were assigned a score of 20. Results presented here include (1) mean scores and relative ranking for each of the 20 Health Action Model questionnaire items (Table 15) and mean scores for the nine broad aspects of the Health Action Model represented by the 20 items (Table 16), and (2) correlations between measures of communication network strength and measures of aspects of the Health Action Model (Table 17). Correlations are calculated both for all participants (n - 32) and for all participants excluding individuals affiliated with AIDS Service Organizations (n - 23). Participants identified certain activities as being relatively more important than others (Tables 15 and 16). The two research and analysis activities (1) identifying a specific target audience and (2) seeking information about that particular audience, were rated overall as relatively most important. The overall score for research and analysis was M - 4.41 (Table 16). Goal delineation was rated just below research and analysis with an overall score of M - 5.33. Establishing clear and measurable goals and objectives for the program were the two Table 15. Mean scores for 20 Health Action Model items. 105 Mean Ranklariahle Stare SD 1 Research and analysis--identify target audience 2.63 (2.6) 2 Goal delineation-~clear goals and objectives 4.47 (4.1) 3 Environmental scanning--other programs 5.38 (4.4) 4 Outside participation--target audience members 5.66 (3.6) 5 Goal delineation--measurable goals and objectives 6.19 (4.7) 5 Research and analysis--KABB information 6.19 (4.3) 7 Outside participation--organizations 9.19 (5.8) 8 Outside participation--community members 10.41 (6.0) 9 Environmental scanning--legislation 11.09 (5.2) 10 Product delivery--location of program delivery 11.22 (3.7) 11 Prevention practices--program effectiveness 11.63 (5.2) 12 Evaluation--outcome evaluation 12.75 (3.9) 13 Evaluation--process evaluation 12.78 (4.5) 14 Product delivery--timing of program delivery 13.28 (3.9) 15 Promotion--frequency of promotion 14.50 (3.8) 16 Evaluation-~formative evaluation 14.94 (4.2) 17 Prevention practices--cost-benefit ratio 15.03 (5.0) 18 Audience benefits and barriers--nonmonetary costs 15 13 (4.1) 19 Audience benefits and barriers--monetary costs 15.34 (4.4) 20 Promotion--cost of promotion 16.34 (4.4) Table 16. Mean scores for nine aspects of the Health Action Model. Mean Bankilariahle Stare $12 1 Research and analysis 4.41 (2.9) 2 Goal delineation 5.33 (3.3) 3 Environmental scanning 8.23 (3.6) 4 Outside participation 8.41 (3.5) 5 Product delivery (place) 12.25 (3.3) 6 Prevention practices (product) 13.33 (3.8) 7 Evaluation 13.39 (2.8) 8 Audience barriers and benefits (price) 15.23 (3.8) 9 Communication or promotion (promotion) 15.42 (3.3) 106 activities that comprised this activity. Environmental scanning which included the activities of (1) seeking information about other HIV programs and (2) seeking information about legislation that might impact the HIV program was rated third highest overall scoring M = 8.23. Seeking outside participation was rated only slightly less important at M - 8.41. Outside participation included participation from three different potential collaborators including, in order of relative importance, members of the target audience, members of other organizations, and community members who are not part of the target audience. The five aspects of the Health Action Model that were considered by participants to be relatively less important were product delivery [M - 12.25], prevention practices [M - 13.33], evaluation [M - 13.39], audience barriers and benefits [M - 15.23], and communication or promotion [M - 15.42]. In the programming questionnaire product delivery referred to the location and timing of program delivery, prevention practices referred to the potential effectiveness and cost- benefit ratio of the program, audience barriers and benefits referred to consideration of both monetary and nonmonetary costs to participants, and communication or promotion referred to the frequency and cost associated with promoting the program. Evaluation has three components to it, formative evaluation, process evaluation, and outcome evaluation. Of these, outcome evaluation was rated the highest [M - 12.75], followed closely by process evaluation [M - 12.78], and finally formative evaluation [M - 14.94]. 107 2. Wffleierta Correlation coefficients were computed to determine whether there were any significant relationships between participants' communication networks and the relative importance attached to aspects of the Health Action Model. The level of analysis is the individual. As such, there are 32 participants in total and 23 when those participants employed by AIDS Service Organizations are excluded. Table 17 presents each communication network variable and specifies any of the Health Action Model activities (composed of summing individual items) where a statistically significant correlation was found. Total Contacts The total number of interactions or contacts a participant recorded was negatively correlated with evaluation when those affiliated with AIDS Service Organizations were excluded from the analysis [I (23) - -.42, p,< .05]. A negative correlation with evaluation, comprised of formative, process, and outcome evaluation, suggests that the more contacts an individual has, the more important all aspects of evaluation are perceived to be. The correlation coefficient is negative because low scores for Health Action Model variables represent relatively greater perceived-importance by respondents. 108 Table 17. Health Action Model correlation coefficients. Communication network xariahlL—_ 1 Total contacts (All participants) (Excluding A803) 2 unique contacts (All participants) (Excluding A803) 3 Unique affiliations . (All participants) (Excluding A808) 4 Total minutes (All participants) (Excluding ASOs) (*) p < .05; (**) - p < -01; Health Action Medelxariahle n/s Evaluation-~composite n/s Evaluation--composite n/s Environmental scanning -- other programs Evaluation--process Environmental scanning -- other programs Evaluation--composite Evaluation--process Environmental scanning -- other programs Evaluation--composite (n/s) not significant (p > .05) (***) - p < .001; Correlation eeeffieient r - -.42‘ r - -.45‘ r - .45' r - -.46“ r - .44' r - -.41‘ r - -.52“ r - .49' r - -.49' 109 Table 17 (cont'd). 5 Average minutes (All participants) Outside participation -- organizations r = .36‘ Communication/promotion-costs r = -.37' Outside participation -- composite r - .40' (Excluding ASOs) Communication/promotion-costs r — -.48' 6 Minutes related to HIV programming (All participants) Evaluation--process r - -.45“ Environmental scanning -- other programs r - .46“ Evaluation--composite r - -.38‘ (Excluding ASOs) Evaluation--process r - -.51' Environmental scanning -- other programs r - .50' Evaluation--composite r - -.44‘ 7 Affiliation type of interactant (All participants) Evaluation--outcome r - -.66“' Outside participation -- target audience members r - -.48“ Communication/promotion-costs r - .39' Evaluation--composite r - -.55“' Communication/promotion -- composite r - .37' (Excluding ASOs) Evaluation--outcome r - -.72“' Outside participation -- target audience members r - -.56“ Communication/promotion-costs r - .47' Evaluation--composite r - -.59“ Communication/promotion ~- composite r - .41' (*) p < .05; (**) - p < .01; (***) - p < -001; (file) not significant (p > .05) 110 unique Contacts The number of diffefen; individuals a participant interacted with was also negatively correlated with evaluation when those employed by ASOs were excluded from the analysis [3 (23) - -.45, p < .05]. Similar results were found for unique contacts as were found for total contacts. Those who interact with a greater number of different people, perceive all aspects of evaluation as being relatively more important than other aspects of the Health Action Model. Unique Affiliations Unique affiliations represents the number of Qiffezen; organizations the unique contacts were associated with. One item, environmental scanning--seeking information about other HIV programs, was significantly correlated with unique affiliations [I (23) - .45, p < .05]. This positive correlation indicates that those who interact with a greater number of different organiZations, perceive information seeking about other programs to be relatively less important than other activities. Total Minutes Total minutes is a measure of the total number of minutes Participants spent interacting with others through either face-to-face or telephone interactions. Results show a significant negative 111 correlation between total minutes and (l) the questionnaire item process evaluation which is a measure of the importance of evaluating activities directed toward meeting the program's goals and objectives (all participants: [1 (32) - -.46, p < .01]; excluding ASOs: [I (23) = -.52, p < .01]), (2) evaluation comprised of all three types of evaluation (all participants: [z (32) - -.41, p < .05]; excluding A803: [1 (23) - -.49, p < .05]), and (3) information seeking (environmental scanning) about other programs (all participants: [1 (32) - .44, p,< .05]; excluding A80: [1 (23) - .49, p < .05]). These results indicate an association exists between total minutes and evaluation such that the greater number of minutes spent interacting with others, the relatively more important evaluation is perceived to be. Results also indicate that the more minutes spent interacting, the less important seeking information about other programs is perceived to be. Average Minutes Average minutes represents the average length of participants' face-to-face and telephone interactions. This communication network variable was significantly correlated with (1) outside participation With members of other organizations (all participants: [1 (32) - .36, D < .05]), (2) the composite measure of outside participation (all Participants: [1 (32) - .40, p < .05]), and (3) promotion costs (all Participants: [1 (32) - -.37, p < .05]; excluding ASOs: [; (23) - -.48, R'< .05]). Results indicate a positive significant correlation for Outside participation such that the longer the average interaction, the 112 relatively less important outside participation, especially with other organizations, is perceived to be. Additionally, results suggest that the longer the average interaction, the more important costs of communication or promotion are perceived to be. Minutes Related to HIV Programming This variable represents the actual amount of each face-to-face and telephone interaction spent "on task” or related to HIV programming. This network variable was significantly correlated with (1) process evaluation (all participants: [1 (32) - -.45, p < .01]; excluding ASOs: [I (23) - -.51, p < .05]), (2) the composite measure of evaluation (all participants: [1 (32) - -.38, p < .05]; excluding ASOs: [I (23) - -.44, p < .05]), and (3) information seeking (environmental scanning) about other programs (all participants: [1 (32) - .46, p < .01]; excluding ASOs: [f (23) - .50, p < .05]). These results indicate that the relatively more time participants spent interacting with others about HIV programming, the relatively more important they perceive evaluation, especially process evaluation, to be. Results also suggest that the more time spent interacting about HIV programming, the relatively less important scanning the environment for information about other programs was perceived to be. 113 Interactant Affiliation Type This variable represents the type of organization interactants were affiliated with. Interactant affiliation was significantly correlated with (1) outcome evaluation (all participants: [1 (32) - -.66, p < .001]; excluding A803: [1 (23) - -.72, p < .001]), (2) the overall measure of evaluation (all participants: [1 (32) - -.55, a < .0011; excluding A805: [1; (23) - -.59, p < .01]), (3) promotion costs (all participants: [1 (32) - .39, p < .05]; excluding A803: [1 (23) - .47, p < .05]), (4) the composite measure of promotion (all participants: [1 (32) - .37, p < .05]; excluding A805: [1 (23) - .41, ;p < .05]), and (5) outside participation from target audience members (all participants: [1 (32) - -.48, p < .01]; excluding A803: [1 (23) - -.56, p < .01]). These results suggest that those who interact with relatively better sources of information, perceive evaluation, especially outcome evaluation, to be relatively less important. The same participants are also less likely to perceive involvement with target audience members as ‘being important. Finally, individuals who interact with relatively ‘better sources of information also perceive program promotion costs as ‘being relatively more important. 3. ReereeaiexLAnalxaia Regression analysis was conducted using the netwOrk variables (1) total contacts, (2) unique contacts, (3) unique affiliations, (4) total 114 minutes spent interacting, (5) average minutes spent per interaction, (6) minutes spent interacting about HIV programming, and (7) interactant affiliation type, as independent variables. Dependent variables, analyzed one at a time, consisted of individual items comprising the Health Action Model programming survey as well as the nine composite aspects of the Model. Variables were entered into the regression equation only if their significance level was less than .05. Results presented here include data from all respondents as well as data when respondents affiliated with an AIDS Service Organization were excluded from the analysis. Four of the seven independent variables considered were included in any of the models that predicted the dependent Health Action Model programming variables. These included total minutes spent interacting, average minutes spent per interaction, minutes spent interacting about HIV programming, and interactant affiliation type. Total contacts, unique contacts, and unique affiliations were not included in any model. Of the dependent variables considered, one or more of the independent 'variables was a significant predictor for environmental scanning, outside participation, communication/promotion of the program, and evaluation . linvironmental Scanning The number of minutes HIV educators spent interacting with others tapecifically about HIV programming was a significant predictor of the extent to which they would likely engage in seeking information about 115 other HIV programs. More time spent interacting about HIV is predictive of less environmental scanning (all respondents: [E (1,32) - 8.21, p < .01]; excluding ASOs: [E (1,23) - 7.14, p < .01]). This was the only dependent variable that the number of minutes spent interacting with others about HIV programming predicted with any significance. The amount of variance explained by this single variable was just over 20 percent for all respondents and approximately 25 percent for those unaffiliated with an ASO. Outside Participation Outside participation by target audience members, outside participation by members of other organizations, and the composite measure for outside participation were each predicted by one or more independent variables. Affiliation type of the interactant was a significant predictor of outside participation by target audience members (all respondents: [E (1,32) - 9.11, p < .01]; excluding ASOs: [E (1,23) - 9.81, p,< .01]). The average number of minutes spent per interaction was a significant predictor of outside participation by members of other organizations (all respondents: [E (1,32) - 4.56, p < .05)]), and both the average number of minutes spent interacting and the total number of minutes spent per interaction were significant predictors of the composite indicator of outside participation (all respondents: [E (2,32) - 6.74, p < .01]). Approximately 32 percent of the variance associated with the composite indicator of outside participation was explained by these two independent variables. For 116 each dependent variable measuring outside participation, relatively better communication networks were predictive of less participation. Communication/promotion For all respondents, interactant affiliation type was the only independent variables that was a significant predictor of both the perceived importance of communication/promotion costs [E (1,32) - 5.22, p < .05] and the overall composite indicator of communication/promotion costs [F (1,32) - 4.75, p < .05]. Approximately 15 percent of the variance for each variable was explained by interaction affiliation type. For respondents unaffiliated with an AIDS Service Organization, both affiliation type and average minutes spent per interaction were significant predictors of the perceived importance of communication/promotion costs [E (2,23) - 9.07, p < .01]. Forty-eight percent of the variance was explained by these two variables. In all cases, interaction with better sources of information is predictive of greater perceived importance associated with the costs of program communication and promotion. Evaluation Affiliation type of the interactant was a significant predictor of outcome evaluation (all respondents: [E (1,32) - 23.01, p < .001]; excluding ASOs: [E (1,23) - 22.64, p < .001]). Total minutes spent 117 interacting with others was a significant predictor of process evaluation (all respondents: [E (1,32) — 7.90, p < .01]; excluding ASOs: [E (1,23) - 7.96, p < .01]). Both interactant affiliation type and total minutes spent interacting were predictors of the composite measure for evaluation (all respondents: [E (2,32) - 11.17, p < .001]; excluding ASOs: [E (2,23) - 11.00, p < .001]). For the dependent variable outcome evaluation, interactant affiliation type accounted for approximately 50 percent of the variance; for process evaluation, total minutes spent interacting accounted for approximately 25 percent of the variance; and the combination of these two variables accounted for about 50 percent of the variance for the composite evaluation indicator. In each case, better communication sources or networks were predictive of greater perceived importance attached to evaluation. 4. ResaltLSamarx Table 18 illustrates the main findings from the investigation. Results center around seven network variables including (1) total contacts, (2) unique contacts, (3) unique affiliations, (4) total minutes, (5) average minutes, (6) minutes related specifically to HIV prevention programming, and (7) interactant affiliation type. The first part of Table 18 (page 118) summarizes results from correlation analyses and analyses of variance conducted to determine the impact of social differentiation and geographic proximity (independent variables) on communication networks (dependent variables). The second part of Table 18 (pages 119-120) summarizes results from correlation analyses and 118 Table 18. Summary of results. Dependent Variables Minutes Total Unique Unique Total Average HIV Affiliation EQDEACL! gopgagfg Effiliatgons M1nutes Minuteg Ee1eted pre l-Wlxaaa Independent Variables Social Differentiation All respondents n/s n/s n/s n/s n/s n/s n/s Excluding ASOs n/s n/s n/s n/s n/s n/s n/s Geographic Proximity All respondents n/s n/s n/s p < .01 p < .05 p < .05 n/s Excluding ASOs p < .001 p < .01 p < .001 p < .01 n/s p < .01 n/s Dependent Variables Minutes Total Unique Unique Total Average HIV Affiliation Cmumua gunman nonhuman: themes humus: sauna. ._JIEL__. Linslznaeflerisnse Independent Variables Social Differentiation All respondents n/s n/s n/s n/s n/s n/s n/s Excluding ASOs n/s n/s n/s n/s n/s n/s n/s Geographic Proximity All respondents n/s n/s ale 2 < .01 n < .01 n < .01 n/s Excluding ASOs n < .01 p < .05 p < .01 p < .01 p < .05 p < .01 n/s 119 Table 18 (cont'd). Independent Variables Minutes Total Unique Unique Total Average HIV Affiliation Cement Quanta (Mtuigdma Enema. human. imaged __1nu__. 3-CemmhuamLAmuxma Dependent Variables Environmental scanning-- other programs All respondents n/s n/s n/s p < .05 n/s p < .01 n/s Excluding ASOs n/s n/s p < .05 p < .05 n/s p < .05 n/s Evaluation-~process All respondents n/s n/s n/s n < .01 n/s p < .01 n/s - Excluding ASOs n/s n/s n/s p < .01 n/s p < .05 n/s Evaluation--outcome All respondents n/s n/s n/s n/s n/s n/s R < .001 Excluding ASOs n/s n/s n/s n/s n/s n/s D < .001 Evaluation--camposite All respondents n/s n/s ale 2 < .05 n/s p < .05 n’< .001 Excluding ASOs p < .05 p < .05 n/s n < .05 n/s p < .05 p < .01 Communication/promotion-- costs All respondents n/s n/s n/s n/s p < .05 ale 3 < .05 Excluding ASOs n/s n/s n/s n/s p < .05 n/s p < .05 Communication/promotion-- composite All respondents n/s n/s n/s n/s n/s ale 3 < .05 Excluding ASOs n/s n/s n/s n/s n/s n/s R < .05 Outside participation-- target audience members All respondents n/s n/s n/s n/s n/s n/s R < .01 Excluding ASOs n/s n/s n/s n/s n/s n/s p < .01 Outside participation-- organisations All respondents n/s n/s n/s n/s n < .05 n/s n/s Excluding ASOs n/s n/s n/s n/s n/s n/s n/s Outside participation-- composite All respondents n/s n/s n/s ale 2 < .05 n/s n/s Excluding ASOs n/s n/s n/s n/s n/s n/s n/s 120 Table 18 (cont'd). Independent Variables Minutes Total Unique Unique Total Average HIV Affiliation gammaa Outage Affliuuhaa Enema. Enema. Mm __kaL__. 4.8muemumLAmuema Dependent Variables Environmental scanning-- other programs All respondents n/s n/s n/s n/s n/s p < .01 n/s Excluding ASOs n/s n/s n/s n/s n/s p < .01 n/s Evaluation--process All respondents n/s n/s n/s p < .01 n/s n/s n/s Excluding ASOs n/s n/s n/s p < .01 n/s n/s n/s Evaluation--outcome All respondents n/s n/s n/s n/s n/s n/s p < .001 Excluding ASOs n/s n/s n/s n/s n/s n/s p < .001 Evaluation--composite All respondents n/s n/s n/s I p < .001 n/s n/s p < .001 Excluding ASOs n/s n/s n/s n < .001 n/s n/s p < .001 Communication/promotion-- costs All respondents n/s n/s n/s n/s n/s n/s p < .05 Excluding ASOs n/s n/s n/s n/s p < .01 n/s p < .01 Communication/promotion-- composite All respondents n/s n/s n/s n/s n/s n/s n < .05 Excluding ASOs n/s n/s n/s n/s n/s n/s n/s Outside participatio -- target audience members All respondents n/s n/s n/s n/s n/s n/s p,< .01 Excluding ASOs n/s n/s n/s n/s n/s n/s p < .01 Outside participation-- organisations All respondents n/s n/s n/s n/s p < .05 n/s n/s Excluding ASOs n/s n/s n/s n/s n/e n/s n/s Outside participation-- composite All respondents n/s n/s n/s p < .01 p < .05 n/s n/s Excluding ASOs n/s n/s n/s n/s n/s n/s n/s 121 regression analyses conducted to determine the impact of differential communication networks (independent variables) on activities associated with the Health Action Model. Dependent variables are included in the second part of the table only where statistically significant results were found. V. DISCUSSION A. Overview The purpose of this investigation was to examine determinants and outcomes of interorganizational communication networks of HIV educators in the state of Wisconsin. There are numerous and diverse HIV/AIDS organizations providing services to both urban and rural areas of the state. In all, 39 organizations were identified that operate a total of 65 HIV prevention programs where (l) at least half of the program's activities focused on HIV prevention, (2) the program had been in operation at least one year, and (3) the program had 10 or fewer paid employees. About three-fourths (50) of these programs were run either by community-based organizations or public health organizations. The audiences most commonly targeted by the programs were those based on age, ethnicity, and drug use including injection drug use. Just as the HIV prevention programs are diverse, so too are the HIV educators providing education. Program personnel represent a variety of (1) ages ranging from 19 to 60, (2) ethnicities including Whites, African Americans, Hispanics, and Native Americans, (3) sexual orientations, and (4) years of experience in the health field ranging from one to 36 years. The prototypical HIV educator in the state of Wisconsin may be characterized as a White, college-educated, heterosexual woman in her mid-to-late thirties with about seven years of experience in health services and four years in HIV education specifically. HIV educators are geographically dispersed throughout Wisconsin. 122 123 Some educators are located in urban areas and provide education within a limited geographic domain, others are located in rural parts of the state and assume responsibility for county-wide education, and still others are located either in an urban or rural area and provide education within multi-county sections of the state. Approximately half of the HIV educators in the state are located in or around two urban geographic locations (Milwaukee and Madison), while the remainder are dispersed throughout the state. Two primary questions addressed in this study concern the impact of (1) social differentiation, the relative degree of similarity with other HIV educators, and (2) geographic proximity, the relative degree of physical distance from other HIV educators, on communication linkages with individuals in other organizations concerning HIV education. Another key question addressed in this study is the relative impact of geographic proximity and social differentiation on interorganizational communication networks. Based on study results, answers to each of these questions are discussed below. Another research question addressed in this investigation concerns the perceived importance by HIV educators of a number of planning activities related to the development, implementation, and evaluation of HIV programs. Just as there are a variety of different types of HIV programs and educators conducting the programs, there are numerous ways in which programs can be operated. The Centers for Disease Control and Prevention has recently developed the Health Action Model that incorporates aspects of program planning that have been shown to be effective in past research. Respondents' relative perceptions of Health 124 Action Model activities are explored. B. The Impact of Social Differentiation on Communication Networks Research Question 1 asks, "What impact does social differentiation have on interorganizational communication networks of HIV prevention educators?" Results here suggest that seeial_differentiatien_has_little WW. Correlation coefficients at the individual level of analysis show no significant association between social differentiation and (1) the number of total contacts, (2) the number of different or unique contacts, (3) the number of contacts within different organizations, (4) the number of minutes spent communicating with others, (5) the number of minutes per interaction spent communicating with others, (6) the number of minutes spent interacting about HIV programming, and (7) interactant affiliation type . Results from the analyses of varianCe also failed to show any significant results for social differentiation. No main effect was found for social differentiation such that those who were relatively more similar or dissimilar to others rated significantly higher on any communication network variable. At the interaction level of analysis there were a few statistically significant correlations. Relatively more similar individuals (1) utilize richer channels of communication, (2) spend more time interacting with others, and (3) perceive interactions to be more 125 important than do relatively less similar HIV educators. It is not surprising that more similar individuals tend to interact through more personal channels such as face-to-face and telephone versus less personal channels such as letters and facsimiles. Two relatively similar individuals are more likely to see each other in some of the same places, and will certainly be more comfortable interacting over the telephone. That more similar HIV educators perceived interactions to be more important probably reflects the perception of similar others that since "they" are "like me," then they probably have valuable information for me to consider. Considering the perspective of dissimilar individuals, they may reason that since "they" are not "like me," then they are unlikely to have much useful information. One can easily imagine an African American bisexual male and former heroin addict currently running a needle exchange program thinking that the prototypical HIV educator in the state of Wisconsin (e.g. female, White, heterosexual, college educated) has little to offer him in terms of his specific HIV prevention program. One final, and interesting result is that HIV educators who were relatively more dissimilar were more likely to utilize better sources of information than were more similar individuals. This result can perhaps be explained by the fact that many of these same individuals interact much less (especially respondents who are also geographically distant) and typically through less rich communication channels than do other HIV educators. Consequently, when they do interact, they strive to communicate with the most potentially useful sources of information available. The overall implication here is that social characteristics of HIV 126 educators have little impact on interorganizational communication networks and therefore in the potential for information dissemination. The fact that some individuals who are dissimilar to most other HIV educators still develop communication networks that are not significantly different, suggests that eee1e1_be111e1e_eeeh_ee_ege1 ‘e_ ; .. - - - .. -,.A . -. ; .. :.. . . . WWW eemmmieatianeitlnotheLLaheaLHILnrearaming- It is important to note that relatively dissimilar HIV educators, and especially those who administer HIV prevention programs to unique population groups, are still likely to perceive information from similar others as more important, and are probably more likely to contact such individuals for specific information related to the design of a particular program for a narrow target audience. However, the importance of the nonsignificant results is in the fact that weak ties, or individuals outside one's own social circle, are more likely to disseminate new information than are strong ties, those within ones social circle. Larger networks are important because they increase the probability of weak tie interactions. C. The Impact of Geographic Proximity on Communication Networks Research question 2 asks, "What impact does geographic proximity have on interorganizational communication networks of HIV prevention educators?" Investigation results clearly indicate that geeg1eph1e ‘ s so,“ so. , -e__:e:0: : me :Q s: q: :0: 127 ef_eemmgn1ee119n_ne1ye1ke. In general, individuals who are physically closer to other HIV educators have better interorganizational communication networks than do other individuals who are less physically proximate. Specifically, correlation coefficients at the individual level of analysis along with analyses of variance indicate that more proximate HIV educators (l) have more total contacts, (2) have more different contacts, (3) have more contacts within different organizations, (4) spend more total minutes communicating with others, (5) spend more minutes per interaction communicating with others, and (6) spend more minutes interacting specifically about HIV programming, than do HIV educators that are less proximate. Correlation coefficients at the interaction level of analysis further indicate that more proximate HIV educators (l) interact with potentially better sources of information, (2) utilize richer communication channels, and (3) perceive interactions using richer communication channels as more important, than do HIV educators that are less proximate. In sum, me1e_p1exime1e_flly -. ; . - -.-.. u. - u- . - ; .- . ._-. .- .HHLJ ; .. .;..- ; . . ; . -; - . ue‘ . u- - :e - . . .4”; .. .;. .. fl1y_egueg1e1e_1he1_e1e_1eee_p1exime1e. Table 19 illustrates mean scores (all participants) for network variables calculated at the individual level of analysis. The first three matrices in Table 19 illustrate differences related to (1) total contacts, (2) unique contacts, and (3) unique affiliations. The sum of Cells A and B are larger than the sum of Cells C and D in all cases, due primarily to respondent differences in Cell D. Differences in total minutes spent interacting with others, average Table 19. Network variable mean scores for all respondents. Similar Social Differentiation Dissimilar Similar Social Differentiation Dissimilar Similar Social Differentiation Dissimilar CeoeraohiLEroaimlu Close Far Cell A Cell D 34 21 Cell B Cell C 38 35 TOTAL CONTACTS CeoaraohiLDroximitx Close Far Cell A Cell D 28 14 Cell 8 Cell C 27 29 UNIQUE CONTACTS CeoamhiLEroximitx Close Far Cell A Cell D 24 11 Cell B Cell C 20 23 UNIQUE AFFILIATIONS Table 19 (cont'd). Similar Social D'EE . I Dissimilar Similar Social Differentiation Dissimilar Similar Social Differentiation Dissimilar 129 Ge 't Close Far Cell A Cell D 1526 500 Cell 8 Cell C 1450 298 TOTAL MINUTES CeoaraohiLEroaimitx Close Far Cell A Cell D 47 26 Cell B Cell C 41 9 AVERAGE MINUTES Ceoaranhieroximiu Close Far Cell A Cell D 1086 405 Cell B Cell C 1267 234 MINUTES HIV RELATED 130 number of minutes spent interacting, and minutes spent interacting about HIV programming, the final three matrices, are extremely revealing. The very large difference between those who are geographically proximate and those who are geographically distant is striking. Respondents in Cells A and B spent about 1500 minutes on average interacting with others while respondents in Cells C and D spent only about 400 minutes on average. WWW.W p1exim111_me11e1e. Figures for the average number of minutes per interaction and the number of minutes spent interacting with others about HIV programming specifically are similarly striking, with those who are geographically closer interacting much more than those who are geographically distant. Results for network variables when respondents affiliated with AIDS Service Organizations were excluded from analysis are presented below (Table 20). When the influence of A808 are removed from analysis, differences in cell means as a result of geographic proximity are more apparent. Whereas results for respondents in Cell C previously were similar to results for respondents in Cells A and B for the variables total contacts, unique contacts, and unique affiliations, results for respondents in Cell C now are identical to those in Cell D. Mean scores for respondents in Cells C and D are much lower than means scores for respondents in Cells A and B, approximately three to four times lower for each variable. Similar results were found for the network variables total Ininutes, average minutes per interaction, and minutes related to HIV 131 Table 20. Network variable mean scores excluding AIDS Service Organizations. E l' E . . Close Far Cell A Cell D Similar Social 43 14 Differentiation Cell 8 [ Cell C Dissimilar 48 14 TOTAL CONTACTS CeoeraohiLDroximitx Close Far Cell A Cell D Similar Social 35 10 Differentiation Cell B Cell C Dissimilar 33 10 UNIQUE CONTACTS CeoeraohiLEroximitx Close Far Cell A Cell D Similar Social 30 8 Differentiation Cell B Cell C Dissimilar 25 8 UNIQUE AFFILIATIONS Table 20 (cont'd). Similar Social Differentiatien Dissimilar Similar Social Qifferentiatien Dissimilar Similar Social Rifferentiatien Dissimilar 132 GO 1.2.. Close Far Cell A Cell D 1976 325 Cell B Cell C 1764 398 TOTAL MINUTES WW Close Far Cell A Cell D 50 25 Cell 8 Cell C 40 29 AVERAGE MINUTES Semathiefmimitx Close Far Cell A Cell D 1410 280 Cell B Cell C 1546 278 MINUTES HIV RELATED 133 programming. Again, mean scores for respondents in Cells A and B are similar as are mean scores for respondents in Cells C and D. For these variables, WLWWMMMM :0; o, u; - - 'u- q -.- 1:1 9' :_ - 0 go - - ooqo‘q ghg_gI§_ggggrgphigailx_di§tant. The analysis excluding respondents affiliated with AIDS Service Organizations clearly illustrates the large main effect for geographic proximity. Aside from some respondents affiliated with AIDS Service Organizations, geographically distant HIV educators probably have less developed communication networks primarily because they do not have as many opportunities to interact with others. D. The Relative Impact of Geographic Proximity and Social Differentiation on Communication Networks Research Question 3 asks, "What is the relative impact of social differentiation versus geographic proximity on interorganizational communication networks of HIV prevention educators?” At least for the group of respondents considered in this investigation, results clearly indicate that WWW WW1 differentiation. While only a few significant results were found for social differentiation, numerous significant results were found for geographic proximity. As such. WW . I! . - no: 0. 9 ‘ o °:q : 01; cum. : 0. q- .0 \- [-1 _qo o~ . " Interestingly, results from the analyses of variance did not show any significant interactions between social differentiation and 134 geographic proximity. This is likely the result of two factors. First, because analyses of variance results for the independent variable social differentiation were all nonsignificant, and second, because the number of respondents in each cell was relatively small so that the interaction would have to be large to see any statistically significant interaction. E. Individual Cell Differences Hypothesis 1 states, ”HIV prevention educators who are both dissimilar to, and geographically distant from other HIV prevention educators will have less developed interorganizational communication networks than will other HIV prevention educators." Eg;_mg§t_ngtxg;k nflWMiflmflWrted. ReSPondents in Cell C (HIV educators who were relatively dissimilar and geographically distant) interacted with relatively poorer sources of information and relied on relatively less rich communication channels. Further, these respondents spent much less time interacting with others, less time interacting with others specifically about HIV programming, and less time on average per interaction. Contrary to what was predicted in Hypothesis 1, however, respondents in Cell C, did have as many or more total contacts, unique contacts, and unique affiliations as did respondents in Cells A, B, or D. This is significant because it suggests that even HIV educators who are both relatively dissimilar and geographically distant can have communication networks with the same degree of breadth and depth as other HIV educators. The extremely low number of minutes per 135 interaction may be accounted for by either social differentiation or geographic proximity, the independent variables. It may be, for example, that while being dissimilar does not hinder the educator's ability to establish communication links, it may hinder lengthy interactions. Dissimilar educators simply may not have enough common ground to sustain interactions for more than a short period of time. The result may also be explained by geographic barriers. Respondents in Cell C may not have engaged in lengthy interactions because it was too costly to do so. Interactions may have been initiated for a particular reason, such as to obtain information about a particular intervention, and discontinued immediately after the desired information had been obtained due to telephone costs. It should be noted, however, that when respondents affiliated with ASOs were excluded from analysis, then mean scores on network variables for individuals in Cell C were lower and resembled those in Cell D more than mean scores for individuals in Cells A or B. Hypothesis 2 states, ”HIV prevention educators who are both similar to and geographically close to other HIV prevention educators will have more developed interorganizational communication networks than will other HIV prevention educators.” Reauita_£rgm_thi§_§tudx_ngxidg nattieLanmrLfenthithmtheaie. Respondents in Cell A averaged more minutes per interaction than did respondents in the other cells and also averaged a higher total number of minutes spent interacting. Interestingly, HIV educators in Cell B, not Cell A averaged the most minutes specifically related to HIV programming. Since respondents in Cell A are more similar to other HIV educators than are respondents in 136 Cell B, it is likely they have more things in common besides HIV education and therefore spend more time talking about other issues unrelated to HIV programming. Individuals in Cell A, however, did not have more total contacts or more unique contacts than did respondents in both Cells B and C. Respondents in Cells A, B, and C all had very similar scores for these network variables, except when ASOs were excluded from the analysis in which case only mean scores for Cell B were similar to those in Cell A. Hypothesis 3 states, "HIV prevention educators who are either dissimilar to, but geographically close to other educators 9; who are similar to other educators, but geographically distant, will have interorganizational communication networks that are developmentally between those who are dissimilar and geographically distant and those who are similar and geographically close.“ This hypothesis refers to .respondents in Cells B and D respectively. Refinit§_grggide_mixgg annpgz;_fgx_1hi§_hyngthefiifi. This hypothesis suggests that results for respondents in Cells B and D will fall between respondents in Cells A and C. There is not support for this hypothesis when considering the network variables total contacts, unique contacts, and unique affiliations. Respondents in Cell D, not Cell C have the lowest number in each category, while respondents in Cell B have very similar numbers to respondents in Cells A and C. Respondents in Cell B, in fact, have the highest number of total contacts. Once again when the influence of respondents affiliated with AIDS Service Organizations is eliminated, however, results for Cells A and B are similar, as are those for Cells C and D. 137 Results for the total number of minutes per interaction and the average number of minutes per interaction lend support to this hypothesis. The average total number of minutes per interaction was 1526 for respondents in Cell A and 298 minutes for respondents in Cell C. Individuals in Cells B and D had mean scores of 1450 and 500 respectively. For the average number of minutes per interaction, respondents scores were 47 minutes for Cell A and 9 minutes for Cell C, and were 41 minutes for Cell B and 26 minutes for Cell D. For each of these two network variables, results were as predicted although the scores for respondents in Cell B were much closer to respondents in Cell A than they were to respondents in either of the two other cells. Results for the network variable that considers the actual amount of time spent on issues related to HIV programming place respondents in Cell D between respondents in Cells A and C as was predicted. Respondents in Cell B however, had the highest average number of minutes, even higher than the average number of minutes for respondents in Cell A. The fact that respondents in Cell B had a higher average than did respondents in Cell A is likely due to the fact that individuals in Cell A are relatively more similar to other HIV educators and therefore will likely have more shared interests to discuss beyond HIV prevention. As previously stated, respondents affiliated with ASOs influence these results. When their impact is removed, mean scores for respondents in Cell B are not too different from respondents in Cell A, both of which are higher than mean scores for respondents in Cells C and D. 138 F. Health Action Model Components Research question 4 and hypothesis 4 each concern the impact of interorganizational communication networks on perceptions of Health Action Model activities. Prior to discussing each of these, general results of the Health Action Model programming survey are discussed. Results of the relative ranking of the nine composite aspects of the Health Action Model suggest that respondents may have ranked these based on time-ordering, that is, the point in time at which they are likely to carry out the activity, rather than the overall relative importance of each, as was intended. This may be the case because the four aspects rated as the relatively most important (research and analysis, goal delineation, environmental scanning, and outside participation) all are typically engaged in prior to determining specific aspects of an intervention, such as the 4 "P8" of marketing, place, product, price, and promotion. Each of these last four aspects were rated as relatively less important than the first four, as was evaluation. G. The Relationship between Communication Networks and Components of the Health Action Medal 1. WWW; Hypothesis 4 states, ”HIV prevention educators who have more developed interorganizational communication networks will potentially incorporate more Health Action Model activities in programming efforts 139 than will HIV prevention educators who have relatively less developed communication networks.” As stated, this hypothesis suggests that respondents who interact more with others will indicate that relatively more of the Health Action Model aspects are important and would incorporate them in the design of an HIV program. flg_5gppgI1_ga§_fggnd_fg;;flypgthe§is_4. Overall, there was little difference in the total number of Health Action Model activities respondents considered to be important and assigned a score to. Fifteen of the thirty-two respondents assigned a score to all twenty activities, and the mean number of activities ranked by all respondents was eighteen. Correlation coefficients between network variables and the number of activities ranked were all generally positive and in the range of .10 to .15, but too low to be statistically significant. At a minimum, these results suggest that almost all HIV educators perceive much value in the Health Action Model activities. 2. Ihe_Imaaet_ef_9ennunieatien_Netuerks.en.fll¥_£resranmins Research question 4 asks, ”What impact will relatively more or less developed communication networks of HIV educators have on the perceived importance of Health Action Model programming activities?” Correlation coefficients were determined and regression analysis run for communication network variables (independent variables) and aspects of the Health Action Model (dependent variables). Results suggest that. in general. mere_dexelenee_eemmunieatien 140 1‘ .o .~ : - : _. . -q ._ c -a '9 -1.‘ 59 ,- oe -' 7' u-- 1 e 0. exaiuatign. This can likely be explained by the importance attached to all types of evaluation in the area of HIV prevention as well as other areas of public health. Results from both the correlation analysis and the regression analysis suggest that better communication networks are associated with less environmental scanning of other programs. This seems unusual since more and longer contacts enable HIV educators to learn more about other HIV programs. This finding may be explained by the fact that those who have more contacts take them for granted and therefore do not perceive a need to find out about other programs; they already know about them. Seeking outside participation from others was positively correlated with average minutes per interaction and negatively correlated with the affiliation type of the interactant. These results suggest that longer interactions with individuals and interactions with potentially better sources of information, are associated with less perceived importance of individuals in the program from outside the organization. Regression analysis supported this finding. One possible explanation for these findings is that HIV educators who interact quite a bit with good sources of information may believe that they constantly obtain input and feedback from others without needing to establish a formal collaborative working relationship. VI. RECOMMENDATIONS Recommendations based on results of the present investigation for improved HIV programming are presented below. Some recommendations are specified at the programmatic level while others are specified at a higher level of HIV planning. A. Geographic Proximity -. ; . ; ;. no - _ .- - .. . - . -;. ; ..; .HMLA ; .. netngkfi. For each network variable considered, a significant difference was found between those respondents who were relatively more physically proximate and those who were not. In each case, those who were more proximate had better communication networks. During the data collection period, these respondents had more total contacts, more unique contacts, and more unique affiliations. Further, these respondents spent more time interacting with others, more time interacting with others specifically about HIV programming, and interacted with potentially better sources of information. Because information about HIV programming is most often disseminated and shared through interpersonal contacts, communication networks are extremely important for effective HIV prevention education. 142 C ev ' ' g9mmunitx;hg§gd_pianning_effgrt. In an effort to localize HIV education in the United States, the Centers for Disease Control and Prevention has mandated a community-based planning effort meant to bring more small community-based organizations and individuals from infected and affected communities into the HIV prevention planning process (Valdiserri, Aultman, & Curran, 1995). The CDC has left it to each state to determine how best to accomplish this goal and, not surprisingly, different states have adopted different planning models. The state of Wisconsin, for example, has one statewide planning group that oversees the entire planning process for the state. The state of Michigan, on the other hand, has divided the state into eight separate regions, each compiling its own HIV prevention plan, which are then integrated by a state planning group comprised of regional members. Besides bringing HIV prevention planning to the local level, the process as carried out in Michigan has brought HIV educators from all over the state together to work on HIV programming issues. Many HIV educators interact with geographically distant others either on statewide subcommittees or as regional representatives at statewide meetings. W W W. This could be accomplished by dividing the state into separate regions as was done in Michigan. Another way to accomplish this is to establish a variety of task forces across the state organized around certain at—risk target 143 populations of interest, with each task force sending one liaison to the statewide group for representation. Each task force might recruit HIV educators from a certain geographical radius to help in the planning effort. Individuals would then have an opportunity to interact with other HIV educators that they normally would not have an opportunity to interact with. Additionally, if a rotating liaison schedule was implemented, several people from each task force could have the opportunity to attend and participate in statewide HIV planning meetings, further opening doors to HIV educators normally outside their realm of interaction. Still considering the CDC community-based planning effort, another way to increase interaction of geographically distant HIV educators might be to convene the statewide planning meeting in a variety of areas around the state and invite geographically proximate HIV educators to . participate in the meetings. This would not only bring HIV educators from certain proximate geographical areas together occasionally, but would also enable them to interact with members of the state planning group at intermittent times throughout the year. 0.0 :10 ’g o ‘ ‘q :.o o_: q o no 0 q- 0mm”. : o. eigggxgnianaii. A computer support group would enable any HIV educator from around the state to inquire electronically about any HIV-related issues including primary and secondary prevention efforts, specific at- risk target audiences, specific HIV education programs such as needle 144 exchange, partner notification, or bilingual street outreach, evaluation instruments, or a variety of other topics. Other individuals involved in HIV prevention from around the state, or perhaps even from other states could respond to any inquiry. Electronic mail would enable ' individuals to interact with specific others by dictating specifically the person or persons to whom the message should be sent. Specialty groups often form within these types of systems. For HIV educators, specific groups may form about specific at-risk populations such as injection drug users, African Americans, or men who have sex with men. Other groups may form around types of interventions such as counseling and testing, peer education, or public information campaigns. With the necessary hardware in place, HIV educators could also link up to other networks outside the state of Wisconsin. Such networks ‘would enable HIV educators from Wisconsin to interact with HIV educators from all over the country and also to access information they otherwise Inight not receive. One network, for example, reviews published literature related to HIV/AIDS and provides a daily synopsis of new articles for HIV/AIDS service providers to review. Computer-based interaction such as that described above requires participants to have a computer, along with the appropriate hardware and software. This is an important consideration because HIV educators who are the most geographically distant are also most likely to be the individuals without the requisite technology. Additionally, this type of communication medium is less rich than either face-to-face or telephone interactions because nonverbal ques are absent and feedback is delayed. Nonetheless, with the appropriate technologY, computer-based 145 interaction is fast, reliable, and fairly inexpensive especially for HIV educators who may be 100 or more miles away from their nearest colleague. WWW- HIV educators affiliated with Aids Service Organizations in geographically distant areas had more developed networks than did HIV educators in rural areas unaffiliated with an ASO. AIDS Service Organizations in rural areas, serve as focal organizations within geographic regions because they focus HIV prevention efforts throughout the entire region, and also in some cases because the A80 is formally affiliated with a very large HIV service provider in the state. In either case, representatives of A805 could communicate with other HIV educators in the region by forwarding copies of certain interesting materials they receive, by organizing a speaker's series to which regional HIV educators would be invited, and by purposefully including other HIV educators in the conceptualization, design, implementation and evaluation of region-wide HIV prevention efforts. B. Social Differentiation o ‘ ‘q on Q‘ s o o ‘u : y ‘07 :0 go“ o 0‘ “no 0 ‘0‘ '0‘ WW. Previous literature suggests that 146 HIV educators should be similar to target audience members so they can more fully understand their attitudes and behaviors and be more sensitive towards cultural norms (Dearing et al., 1995). As such, former Vietnamese prostitutes educate current Vietnamese prostitutes, African American men who used to inject drugs provide education to African American male injection drug users, and gay white teens provide peer education to other gay white teens. When the intended audience is general, so too will be the HIV educators. However, when the audience is narrow and the groups marginalized as in the examples above, then the HIV educator will, by definition, be different from most other HIV educators, especially in a state such as Wisconsin where the average audience is targeted by just over two demographic, behavioral, or situational identifiers. Results from the present investigation suggest social differentiation has little impact on interorganizational communication networks. Since communication networks are unaffected by "who" the HIV educator is, homophilous HIV educators can, and should, be hired, where appropriate, without concern that the educator will be isolated from other HIV educators. Special consideration should be given to the educator who is geographically distant as well as socially differentiated though since they interact with others less than any other type of educator. 147 C. Miscellaneous own-oo-c q; z . -d- . . . - z .7 1 an . 1 ] Ei 1i .1 J E I] HIEUEIDS i Qrganizatigns. Results show that almost half of respondents' interactions were with individuals who were either affiliated with an organization whose primary focus was not related to health or with individuals who were not affiliated with any organization. While it is true, that much can be learned from individuals such as these, it is also true that the majority of information related to the conceptualization, design, implementation, and evaluation of HIV education programs will come from personal experience and information obtained from other HIV educators. Results of the present study support this conclusion as evidenced by the greater importance respondents placed on interactions with HIV/AIDS service providers and other health service providers that do not focus specifically on HIV/AIDS. Hiy_edugatgrg. HIV educators that have been in the area of HIV prevention the least, are those that interact with more total contacts, more unique contacts, and more unique affiliations. This may be explained by the fact that they are new to the field and have much to learn. At some point, however, it seems that HIV educators decrease interaction, perhaps because they have learned much of what they need to 148 in order to perform their job. Even if this is true, HIV educators should continue to interact with others in order to keep abreast of continually changing (1) political climates, (2) funding opportunities, (3) efficacious prevention efforts, and (4) at-risk population groups. In short, HIV educators must not stop learning how to provide the most effective HIV prevention programs possible. ,.— - onn‘qe‘o ,. , -._ :_ . .; no - ;_ -. .. Wan—am. Results from the present investigation indicate that HIV educators pay particular attention to the early states of program conceptualization, but do not consider details about program design and evaluation to be nearly as important. While much early work done in the program conceptualization stage can enhance HIV programs, many important considerations also need to be made concerning the aspects of the program related to the 4 "P5" of social marketing, specifically product, price, place, and promotion. Additionally important considerations must also be made related to all aspects of evaluation, formative, process, and outcome. Solid evaluation will not only enhance program outcomes to impact behavior change, but will also increase the likelihood of continued or additional funding since evaluation has become a requirement by many funding sources . 149 D. Future Research Results from the present investigation suggest that communication networks of HIV educators can be predicted based on geographic proximity, but less so based on social differentiation. Future research should consider whether a similarly strong relationship exists for geographic proximity in other areas of health promotion such as smoking cessation and nutrition, as well as in other areas specifically related to sexually transmitted diseases. More research is also needed to determine if the null results found here for social differentiation are unique to HIV prevention educators or if they can be generalized to other areas of disease prevention and health promotion. That is, there might be certain unique characteristics of HIV educators vis a vis other health educators that enable them to look beyond demographic characteristics when communicating with others. Future studies should also be conducted to determine if results found in this investigation remain consistent in individual cities where HIV prevention is critical such as in San Francisco, New York, and Miami. Future research might consider whether the highly competitive nature of HIV funding mediates communication network development, especially with respect to social differentiation. The extent to which geographic barriers are created by HIV prevention programs within these cities might also be investigated. Finally, additional research is needed to more conclusively 'determine the impact of differential communication networks. While some evidence was found for greater perceived importance of certain 150 strategies associated with the design, implementation, and evaluation of HIV prevention programs, future studies might test the impact of communication networks on other health promotion models or specific program strategies. Future research might also be designed to focus on agtgai_u§g of certain strategies, rather than on the peggeixgd impgxfiangg of specific strategies as was done in the present investigation. APPENDICES APPENDIX A INTRODUCTORY LETTER AND STUDY DESCRIPTION Date Name, Title Organization name Address Address City, State, Zip code Dear Name: My name is Gary Meyer and I am presently a student at Michigan State University. I am beginning my dissertation study and with it hope to learn more about HIV prevention education programs in the State of Wisconsin. I have attached a brief description of the study for your information. My hope is that all HIV/AIDS programs in Wisconsin can benefit from the study’s results which I will present at a meeting in Wisconsin next summer. I plan to contact you by telephone to ask you a few questions about the HIV /AIDS programs offered by your organization. This call should take only a few minutes, but it is nonetheless a very important first step in the study. I would like to take this opportunity to thank you in advance for your help. I look forward to speaking with you soon. Cordially, Gary Meyer lSl 152 DESCRIPTION OF STU DY Purpose: This study is being conducted to learn more about HIV prevention education programs in Wisconsin. Sixteen HIV prevention programs from around the state will be included in the study which focuses on predictors and outcomes of interorganizational communication networks. Results from the study will provide important theoretical and applied lessons concerning HIV programming. Length: The duration of the study is approximately one year beginning August 1, 1994 and ending July 31, 1995. Funding: Funding for the study has been obtained from the United States Agency for Health Care Policy and Research in the form of a doctoral dissertation grant. All information obtained from participants in this study will be kept strictly confidential. Only Mr. Meyer, the Principal Investigator, will have access to the data. The Principal Investigator will not compromise the integrity of any individual or organization. All data will be reported as group data-individual and organizational names will not be reported. This research project is being overseen by Dr. James W. Dearing, an Assistant Professor in the Department of Communication at Michigan State University. Dr. Deming is the Chairperson of Mr. Meyer’s dissertation committee. You may feel free to contact Dr. Dearing at 517/355-1820 should you have any questions or concerns related to this project. APPENDIX B PROGRAM IDENTIFICATION & DESCRIPTION QUESTIONNAIRE PROGRAM IDENTIFICATION & DESCRIPTION QUESTIONNAIRE Name of organization: Phone number: Fax number: Name (of individual completing survey): Position: Hi , my name is Gary Meyer and I am calling from Michigan State University. This past week I sent you a letter briefly describing my dissertation study. Do you have any questions about the letter? (If yes, address them; if letter was not received, briefly explain project). Would you mind answering a few questions today? (If yes, ask if there is a better time, or if applicable, ask if there is someone else to speak with). In the first part of this project I have identified all organizations and their programs within the State of Wisconsin engaged in HIV prevention programming. The purpose of today’s call is to learn more about your HIV prevention program(s). Your answers to the following questions signify your voluntary agreement to participate. This call will only take a few minutes and will not be recorded. You can choose not to answer any of the questions and you are free to stop the interview at any time. Does your organization have any programs that have been in existence at least one year and where the majority of the effort (that is, over half) is aimed at HIV prevention education among uninfected persons? (If yes, continue) (If no, thank participant for his/her time and terminate interview - this organization and its programs will be excluded from this investigation) Program name(s): 153 154 Organization: Alright, first I would like you to describe one program offered by your organization where the majority, that is, over half of the effort is aimed at HIV prevention. Before you do though, please tell me the name of the program. Program name? Program number: What geographic area does thisprogram serve? How long has this program been in operation? _ How many paid staff members work on this program? How many volunteers work on this program? Wlmt is the current year’s budget for this program? 3 Now, please describe this program to me. (Focus on goals, objectives and methods, and note target audience if possible) 155 Organization: Program Name: Program Number: State only the first time: I am going to ask you questions about the group of individuals that you are intentionally trying to reach with your program. Some of these population characteristics are behaviorally based, others are not. For example, in Detroit, an AIDS brochure published by a Latino Family Service organization is read by men, women, and people of various ages and sexual orientation, but the intent of the designers was to target young Hispanic gay men. Therefore, we would consider the target population to be young Hispanic gay men. Place a check mark next to each of the following target group criterion that is specifically targeted by the organization. Then, write down specific categories under each criterion as specified by interviewee. Do you target by. . . 1. Gender? Yes 9. Hemophiliac? Yes _ 2. Age? Yes 10. IV drug use? Yes _ 3. Education? Yes 11. Other drug use? Yes _ 4. Ethnicity? Yes _ 12. Prostitution? Yes _ 5. Homeless? Yes 13. Sexual orientation? Yes _ 6. SES? Yes _ 14. Other? Yes _ 7. Citizenship? Yes _ 15. Other? Yes _ 8. Primary language? Yes 16. Other? Yes __ Subtotals: TOTAL: Organization: Program Name: Program Number: I would like to verify the communication channels and methods your organization utilizes in its educational efforts (place an ”X” next to each channel and method). COMMUNICATION CHANNEL Face-to-face DELIVERY METHOD Small group large group Skits; theatrical Street outreach Counseling Other COMMUNICATION CHANNEL Telephone DELIVERY METHOD Hotline Info; referral Other COMMUNICATION CHANNEL Audio-visual DELIVERY METHOD PSA TV program Video tape Movie Other COMMUNICATION CHANNEL Print DELIVERY METHOD Brochure Book Poster Newspaper Magazine Comic strip T-shirts Buttons Other COMMUNICATION CHANNEL Radio ' DELIVERY METHOD PSA Talk show Program Other COMMUNICATION CHANNEL Computer DELIVERY METHOD BBS Software Other 157 Organization: Program Name: Program Number: If the program has been operating at least 1 year, has less than 10 staff people and provides education in a single geographic area, then continue with this page. This is just the type of program that I am interested in for this study. May I send a short survey to you and all program personnel? The questions relate primarily to social demographics. (If yes, obtain names of program personnel; If no, stress the importance of this step in the investigation and ask for participation again). I will mail a survey to each individual separately and enclose a stamped, self- addressed envelope for convenience and confidentiality. Verify address(es) to send surveys. Address: Address: Address: City: State: Zip: Notes: APPDIDIX C DEMOGRAPHICS QUESTIONNAIRE DEMOGRAPHICS QUESTIONNAIRE last four digits of your social security number: 1. What is your gender? Female Male 2. What is your age? __ 3. What is the highest level of education you have completed? Less than high school degree High school degree Associates degree College degree Graduate degree 4. What is your sexual orientation? Heterosexual Homosexual Bisexual 158 159 5. What is your ethnicity? Caucasian Native American Afiican American _ Asian/Pacific Islander Hispanic Other 6. How long have you been employed by this organization? years a g 7. Approximately how many miles do you live from your office? miles “if 4.? 8. How long have you been a paid employee working in the health education field? years 9. How long have you been a paid employee working in the area of HIV/AIDS education? years 10. In general, how similar do you think you are to other individuals in the State of Wisconsin working in the area of HIV] AIDS education? Very similar Somewhat similar Somewhat dissimilar Very dissimilar 160 11. In general, how similar do you think you are to other individuals in the State of Wisconsin working in the field of health education, but not specifically in the area of HIV/AIDS education? Very similar Somewhat similar Somewhat dissimilar Very dissimilar 12. Compared to other individuals in the State of Wisconsin working in the area of HIV/AIDS education, I have... More status Same amount of status Less status 13. It is common for me to socialize with other individuals in the State of Wisconsin working in the area of HIV/AIDS education at events that are unrelated to work. Yes No 161 14. List the five most important sources of information (knowledgeable people) outside of your organization who could help you design, implement, and / or evaluate this program. Individual Organization i Thank you for completing this questionnaire. Please place in the stamped, self- addressed envelope provided. APPENDIX D COMMUNICATION NETWORK LOG FRONT OF COMM UNI CA TION LOG Date: ID # ’ I I * “OGAIZT’ NAME TYPE (see list below) CANHEL MNTES or PAGES RELATED ‘ _ Face-to—face 25 50 i Phone 1 Fax / Letter m1. ' 1 - HIV/AIDS health service provider 4 - HIV/AIDS health service provider 2 - Non-HIV/AIDS health service provider 5 - Non-EIV/AIDS health service provider 3 - Non-health service provider 6 - Non-health service provider BACK OF COMM UNI CA HON LOG When complete, please COPY and return to: THANKS FOR Gary Meyer Department of Communication REMEMBERING TO Michigan State University East Lansing, MI 48824-1212 COMPLETE THIS For questions or comments CARD TODAY Gary Meyer may be reached at 517/351-4037. 162 APPENDIX E PROGRAMMING QUESTIONNAIRE FINAL SURVEY HIV PREVENTION EDUCATION PROGRAMS IN WISCONSIN Dear (Name of Respondent): Many thanks for your dedicated support of my dissertation research project concerning HIV prevention programs in Wisconsin. I have just one last request of you, and that is to complete the enclosed survey, an exercise that I think you will find both interesting and fun. This exercise will take only lO-15 minutes to complete. As always, all responses are strictly confidential. I am the only one who will have access to your answers. To begin this exercise, imagine that you have been hired to start a new HIV prevention education program similar to the one you currently work with. Imagine that you face the typical constraints of normal program development, but that you have ample funding for the first three years of the program. In fact, assume you have enough funding to engage in each of the 20 activities listed on page 3. For this exercise, I would like you to rate the relative likelihood that you would engage in each of the activities on page 3 by placing the stickers/numbers (found at the top of page 3) next to each of the activities. Each activity is one that you may engage in as you begin to design and implement your hypothetical program. Although you mayfeelthatenchactivityisimportant, Iaminterestedintherelatiyeimportance that you give to each activity. Therefore, place the sticker with the number 1 next to the activity that you feel is MOST important and would definitely engage in, the sticker with the number 2 next to the activity that you feel is the next most important and would engage in, and so on. If you come across one or more activities that you would not engage in, then do not place a number (sticker) next to the activity. Simply, leave a blank space. Please be sure to read all statements carefully (at least once) before assigning any numbers. You may want to use a pencil to fill in the numbers before affixing the stickers. A sample exercise is provided on page 2. Researcher note: IHERE ARE NO RIGHT 0R WRONG ANSWERS. I am only interested in your honest responses. 163 164 W I have listed five activities below that a person might engage in while seeking a job. You will notice that I have placed the numbers 1, 2, and 3 next to three of the activities and left two of the activities blank. The activity with the number 1 next to it is the activity that I consider to be the most important and is one I would engage in. The activity with the number 2 next to it is the activity that I consider to be the next most important and I would engage in it as well. The activity with the number 3 next to it, is one that I consider least important W. Note that each activity with a number next to it is one that I would engage in. The two activities without a number next to them are activities that I would not engage in. Therefore, I did not place a number next to them. Obtain letters of recommendation from three individuals. Contact a temporary employment agency. Notify friends and family that I am on the job market. Seek professional help from a resume specialist. Monitor the local newspapers for job postings. I would like to thank you in advance for your continued support! Your responses are very important to me and therefore I have enclosed several new AIDS Awareness Trading Cards as a small token of my sincere appreciation. When you have finished the exercise on page 3, please place it in the envelope provided. I WILL PICK UP THE EXERCISE ALONG WITH THE COMMUNICATION LOGS BEGINNING THE WEEK OF MAY 1, 1995. If you do not plan to be in the office during the first few weeks in May, please leave the materials with someone who will be able to give them to me. As always, feel free to contact me at 517/351-4037 with any questions, comments, or concerns. Cordially, Gary Meyer Michigan State University 165 PROGRAM ACTIVITIES Solicit participation from community members/ leaders (who are not in the target audience) to assist in program design and implementation. Design measures to evaluate the program’s outcomes (outcome measures). Determine how frequent program promotion should be. Determine the location (one or more sites) for program delivery. Assess nonmonetary costs to program recipients such as time, psychosocial, and physical costs. ‘ Compute the cost~benefit ratio for the HIV prevention program. Establish measurable goals and objectives for the prevention program. Solicit participation from target audience members to assist in program design and implementation. Design measures to evaluate the program’s activities (process measures). Obtain data about the knowledge, attitudes, beliefs, and behaviors of the target audience. 166 Seek information about existing/proposed legislation that may impact the HIV prevention program. Calculate monetary costs to the program’s recipients. Analyze the potential effectiveness of the HIV prevention program. Establish clear goals and objectives for the HIV prevention program. Solicit participation from members of other organizations/agencies to assist in program design and implementation. Pre-test one or more aspects of the program prior to full program implementation. Assess the cost of promoting the program relative to its potential effect. Determine the timing (when and how often) of program delivery. Clearly identify the target audience for the HIV prevention program. Seek information about other similar HIV prevention programs. BIBLIOGRAPHY Bibliography Adams, J.S. (1980). Interorganizational processes and organization boundary activities. In L. Cummings & B. Staw (Eds.), Eggggggh_in nrganizatienal.heha¥ier (Vol 2, pp. 321-355). Greenwich, CT: JAI Press. Agency for Health Care Policy and Research. (1990). flggi;h_§g;gigg§ reaeareh_en_HIYAAIDfiereleted_illneeeee. Washington: U.S. Department of Health and Human Services. Agency for Health Care Policy and Research. (1992). Health_§erxiges zggga;gh_gn_zu;gi_hgaith. Washington: U.S. Department of Health and Human Services. Agency for Health Care Policy and Research. (1993). Grant§_f9;_h§gi;h aerxieee_diesertatien_researeh. Washington: U-S- Department of Health and Human Services. Aldrich, H. (1978). Organization sets, action sets, and networks: Making the most of simplicity- In Handheekzfer_etsanizatienal_desizn. P. Nystrom & W. Starbuck (Eds.). Amsterdam: Elsevier Scientific. Aldrich. H.E. (1979). Qtsanizatiens.and_enxirenmenta. Englewood Cliffs, NJ: Prentice-Hall. Aldrich, H., & Whetten, D.A. (1981). Organization-sets action-sets, and networks: Making the most of simplicity. In P.C. Nystrom & W.H. Starbuck (Eds.). Handhaek_ef_ersanizatienal_desisn (Vol. 1. PP- 385-408). New York: Oxford University Press. Alinsky, S.D. (1946). Reyeiiie_figr_radigai§. Chicago: University of Chicago Press. Allen, T.J. (1993). Managing_the_figw_g£_teghngiggy. Cambridge: The MIT Press. Barnlund, D.C., & Harland, C. (1963). Propinquity and prestige as determinants of communication networks. figgigmetzx, 26, 467-479. Benson, J. (1977). Innovation and crisis in organizational analysis. Seeielesieal_9uarterlx. 18. 3-16. Bernard, H.R., & Killworth, P.D. (1978). A review of the small world literature. anngggignfi, 1, 45. Bernard, H.R., Killworth, P., & Sailer, L. (1981). Summary of research on informant accuracy in network data, and on the reverse small world problem. anngggigna, 4(2), ll-25. 167 168 Blau, P.M. (1982). Introduction: Diverse views of social structure and their common denominator. In P.M. Blau & R.K. Merton (Eds.), Eentinuitiee_in_etruttural_inauirx- Newbury Park. CA: Sage- Bochner, S., Buker, E. A. , & McLeod, B. M. (1976). Communication patterns in an international student dormitory: Modification of the small world method Ieurnal_ef_Anplied_§eeial_2sxehelesx 6 275 290 Bracht, N., & Gleason, J. (1990). Strategies and structures for citizen PartnershiPS- In N- Brecht (Ed ). Health_atemetien_at_the ngmunity_iexei. Newbury Park, CA: Sage. Brewer. J.. & Hunter. A. (1989). Multinethed_reeeareht_a_axnthesis_ef siyigfi. Newbury Park, CA: Sage. Burt, R.S. (1980). Models of network structure. Annuai_fieyigg_gfi Stainless. 6. 79-141. Burt, R. S. (1983). Range. In R. S. Burt & M. J. Minor (Eds. ), Appiigd Netaerk_Analxaiet_A_nethedelesieal_intreduetien. Beverly Hills CA: Sage. Cain, R. (1993). Community-based AIDS services: Formalization and depoliticization. 1nternational_leurnal_ef_flea1th_§erxieea. 23(4). 665-684. Caplow, T., & Forman, R. (1950). Neighborhood interaction in a homogenous community. AngIiggn_§ggigiggi§ai_fiexiex, 15, 357-366. Centers for Disease Control and Prevention. (1994). HIEAAIDS aurxeillanee_renert. 6. 1-39. Coleman, J.S., Katz, E., & Menzel, H. (1966). Medicai_inngxa§ign;_A diffusign_§tudy. New York: Bobbs-Merrill. CollinS. B E.. & GuetszW. H. (1964). A_aeeial_ns1£heles¥_ef_sreun areeeaaea_fer_deeisien;making- New York: John Wiley & Sona- Conrath, D.W., Higgins, C.A., McClean, R.J. (1983). A comparison of the reliability of questionnaire versus diary data. Sggiai_flg§ngxk§, 5, 315-323. Crane. D (1972) Inxiaihle_eelleaeat_Diffueien_ef_kneuledse_in agigngifiig_ggmmgni§igfi. Chicago, IL: University of Chicago Press. Daft, R.L., & Lengel, R.H. (1984). Information richness: A new approach to managerial information processing and organizational design. In B.M- Stav & L.L. Cummings (Eds.). Researeh_in_ersanizatienal hehaxigz, Greenwich, CT: JAI Press. 169 Dearing, J.W., Meyer, G., & Rogers, E.M. (1994). Diffusion theory and HIV risk behavior change. In R. DiClemente and J. Peterson (Eds.), BellaxieralJnterxentieanemmntexentien. New York: Plenum. Dearing, J.W., Rogers, E.M., Meyer, G., Casey, M.K., Rao, N., Campo, 8., Henderson, G., & Betts, G. (1995). Strategies of HIV prevention programs in San Francisco. Working_£aper. (Available from the Department of Communication, Michigan State University, East Lansing, MI 48824-1212) Delaney, F., & Moran, G. (1991). Collaboration for health: In theory and Practice. HealtILEdueatienmrnel. 50(2). 97-99. Dill, W.R. (1958). Environment as an influence on managerial autonomy. Administratimiciensemarterlx. 2. 409-443. Duff, R.W., & Liu, W.T. (1975). The significance of heterophilous structure in communication flows, 2hiiipping_Qu§;;g11y_gfi_gnl;gzg and_fioeietx. 3. 159-175. Duhl. L. (1986). Healtthanniruaneeiathange. New York: Human Sciences. Durkheim. E. (1951). SuitideLLstndy—inJetielm- New York: Free Press. Eckstein, S. (1977). Politicos and priests: Iron law of oligarchy in interorganizational relations. ngpg;a;iyg_£gii§ig§, 9, 463-481. Eisenberg, E.M., Farace, R.V., Monge, P.R., Bettinghaus, E.P., Kurchner- Hawkins, R., Miller, R., & Rothman, L. (1985). Communication linkages in interorganizational systems. In B. Dervin & M. Voigt (Eds.). WW (Vol. 6. pp. 210-261). Norwood, NJ: Ablex. Farace, R.V., Monge, P.R., & Russell, H.M. (1977). anmunicating_and grganizing. Reading, MA: Addison-Wesley. Farley, R., & Frey, W.H. (1994). Changes in the segregation of whites from blacks during the 1980's: Small steps toward a more integrated society. WW. 59. 23-45. Festinger, L., Schachter, S., & Back, K. (1950). figgiai_pres§ureg_nn W- New York: Harper. Fine. 8.1!. (1981). IhemrketimLideeLaanceiaLiesnes. New York: Praeger. Fischer, C.S., Jackson, R.M., Stueve, C.A., Gerson, R., Jones, L.M., & Baldassare. M. (1977). WWW Lhe_urban_aetting. New York: Free Press. 170 Freire, P. (1970). Pedagogy of tho ooprogoed. New York: Penguin. Friedkin, N.E. (1982). Information flow through strong and weak ties in intraorganizational social networks. Sooioi_flo§go;k§, 3, 273-285. Galaskiewicz, J. (1985). Interorganizational relations. n v' o Seeielesx. 11. 281-304. Galaskiewicz, J., & Shatin, D. (1981). Leadership and networking among neighborhood human service organizations. Adminio;xotigo_§oionoo max. 26. 434-448. Goldstein, E. (1995). [Preferred communication sources of HIV educators]. Unpublished raw data. Granovetter. M S. (1970) ChangingJehquhanneLLoLmhilitx informatieanJJuhurhameemnitx Unpublished doctoral dissertation, Harvard University, Cambridge, MA. Granovetter, M.S. (1973). The strength of weak ties. Amorioon_lournoi_ofi Sooioiogy, 73, 1361-1380. Granovetter. M.S. (1974). gettinuJehLAmdLQLsentastLand ooxooro. Cambridge, MA: Harvard University Press. Granovetter, M.S. (1982). The strength of weak ties: A network theory revisited. In P.V. Marsden & N. Lin (Eds.), Sooioi_§tzuotozo_ono Notwozk_Anoiyoio. Beverly Hills, CA: Sage. Green, L.W. (1986). The theory of participation: A qualitative analysis of its expression in national and international health politics. WW (Vol. 1. PP- 211-236). Greenwich, CT: JAI. Green. L..W &Kreuter. MN (1991) WM WNW Mountain View CA Hayfield- Green, L.W., & McAlister, A.L. (1984). Macro-intervention to support health behavior: Some theoretical perspectives and practical reflections. Healthjdneatiemfluarterlx. 11. 322-339. Greer, A.L. (1988). The state of the art versus the state of the science: The diffusion of new medical technologies into practice. WWW. 4. 5-26. Gullahorn, J.T. (1952). Distance and friendship as factors in the gross interaction matrix. Sooiomotry, 15, 123-134. Htigerstrand. T. (1967). W. Chicago: University of Chicago Press. 171 Hall, R.H., Clark, J. P. Giordano, P. C. Johnson, P. V. & Van Roekel, M. (1977). Patterns of interorganizational relationships. Administratixeefieieneefiuatterlx 22 457 474 Heathcote, G. (1990). Networking: A strategy for the professional development and support of health educators. Hooith_Edooo;ion Journal. 49(1). 27-29. Higgins, C.A., McClean, R.J., & Conrath, D.W. (1985). The accuracy and biases of diary communication data. Sooioi_flo;work§, 7, 173-187. Indyk, D. , & Rier, D. A. (1993). Grassroots AIDS knowledge. Knouioogo; Creatinn..l21ffus.ien...litilizatien.15.3-.43 Jablin, P.M. (1980). Organizational communication theory and research: . An overview of communication climate and network research. In D. i Nimmo (Ed.), Communiootion_yooroook_& (pp.327-347). New Brunswick, “< NJ: Transaction. Jorgensen, G., McKenna, J, & Kingon, R. (1994, May). Iho_flooi§h_5o§ion WWW WW. Paper presented at the Fourth Annual Conference on Social Marketing in Public Health, Clearwater Beach, FL. rur Kandel, D.B. (1978). Homophily, selection, and socialization in adolescent friendships. Amozioon_1ournoi_of_§ooioiogx, 84, 427- 436. Kanter. R.H. (1977). MW. New York: Basic Books. KatZ. D. & Kahn. R. (1978). We Lfiooono_Eoitionl. New York: John Wiley & Sons. KatZ. 3.. &Lazarsfeld. PF. (1955) PersenalJnflueneeifleJartnlaxen W. New York. Free Press. Kelley, H. (1951). Communication in experimentally created hierarchies. W9 49 39-56- Kimberly, J. R. (1981). Managerial innovation, in P. C. Nystrom & W. H. Starbuck (Eds. ). WW ermizatiensmheiunximmenth New York. Oxford University Press. Klonglan, G.E., Warren, R.D., Winkelpleck, J.M., & Paulason, S.K. (1976). Interorganizational measurement in the social services sector: Differences by hierarchical level. Adminiotrooigo_§oionoo W. 21. 675-687. 172 Klovdahl, A.S. (1985). Social networks and the spread of infectious diseases: The AIDS example. fiooioi_§oioooo_flodioino, 21(11), 1203- 1216. Klovdahl, A.S. (1994). Social networks and infectious disease: The Colorado Springs study. Sooioi_§oionoo_flodioioo, 38(1), 79-88. Korzenny, F. & Farace, V. (1978). Communication networks and social change in developing countries- lnternatienal_and_lnteteultural £2mmunieatien.Annual. 4. 69-94. Kotler. P. (1975). Marketins_fer_nenprefit_ersanizatiene. Enslewood Cliffs, NJ: Prentice-Hall. Kotler, P. & Roberto, E.L. (1989). fiooioi_mozko;ing. New York: Free Press. Laumann, E.O., Galaskiewicz, J., & Marsden, P.V. (1978). Community structure as interorganizational linkages. Annooi_fioxioy_o£ Seeielesx. 4. 455-484. Laumann. E.0.. & Pappi. F.U. (1976). Netaerks_ef_eelleetixe_aetien. New York: Academic Press. Lazarsfeld, P.F., & Merton, R.K. (1964). Friendship as social process: A substantive and methodological analysis. In M. Berger and others (Eds ). Ereeden_and_eentrel_in_medern_seeietx. New York: Octagon- Lee, N.H. (1969). Iho_oooroh_foz_on_ohoroioniot. Chicago, IL: University of Chicago Press. Lefebvre, R.C., & Flora, J.A. (1988). Social marketing and public health intervention. Health_Edueatien_Quarterlx. 15(3). 299-315. Lincoln, J.R., & McBride, K. (1985). Resources, homophily, and dependence: Organizational attributes and asymmetric ties in human service networks. Sooioi_§oionoo_3ooooxoh, 14, 1-30. Lionberser. H.F. (1975). Seeial_ehanse_in_eemnunieatien_atrueture1 . Morgantown, West Virginia University, Rural Sociological Society Monograph 3. Liu, W.T., & Duff, R.W. (1972). The strength of weak ties. Euhiio W. 36. 361-366. Logan, J.R., & Molotch, H.L. (1987). flrhon_fioz;unoo. Berkeley, CA: University of California Press. Luke, R.D., Begun, J.W., & Pointer, D.D. (1989). Quasi firms: Strategic interorganizational forms in the health care industry. Aoooomy_ofi flanasement_Bexiew. 14(1). 9-19. 173 Mann, J.M. (1993). We are all Berliners: Notes from the Ninth International Conference on AIDS. Amorioon_lon;nai_o£_£noiio floaith, 83(10), 1378-1379. Manning, P.R., & Denson, T.A. (1980). How internists learned about cimetidine. Annala_ef_1nternal_uediein_. 92. 690-692. Manoff, R.K. (1985). Sooiai_Marko§ing. New York: Praeger. Marsden, P.V. (1990). Network data and measurement. Annual Review of 599121281. 16, 435-463. McKinney, M.M., Barnsley, J.M., & Kaluzny, A.D. (1992). Organizing for cancer control: The diffusion of a dynamic innovation in a community cancer network. Internatienal_leurnal_efffeehnolegx Aaaeaanent_in_flealth_9are. 8(2). 268-288. Means, R., Harrison, L., Jeffers, S., & Smith, R. (1991). Co-ordination, collaboration and health promotion: Lessons and issues from an alcohol education programme. Health_Premetien_lnternatienal. 6(1). 31-40. Merton, R. (1948). The social psychology of housing. In W. Dennis (Ed.), Qurrent_trenda_in_aeeial_psxehelosx- Pittsburgh. PA: University of Pittsburgh Press. Merton, R.K. (1975). Structural analysis in sociology. In P.M. Blau (Ed.). Aanreaehea_te_the_stud¥_ef_aaeial_atrueture. New York: Free Press. Merton, R.K. (1988). Reference groups, invisible colleges, and deviant behavior in science. In H.J. O'Gorman (Ed.), fin:yoying_oooioi iifo, (pp. 174-189). Middletown, CT: Wesleyan University Press. Milardo, R.M. (1982). Friendship networks'in developing relationships: Converging and diverging social environments. fiooioi_£oyohoiogy Quarterly. 45. 162-172. Mintzberg, H. (1973). Iho_notn:o_o£_monogorinl_xork. New York: Harper & Row Publishers. Mitchell, J.C. (1969). The concept and use of social networks. In J.C. Mitchell (Ed.). Seeial_netwarka_in_urhan_aituatians. Manchester. UK: Manchester University Press. Mytinger. R.E. (1968). Innexatien_in_leeal_health_aerxieea. Arlington. VA: Public Health Service, U.S. Department of Health, Education, and Welfare. National AIDS Clearinghouse. (1993). Atlanta: Centers for Disease Control and Prevention. 174 National Center for Health Statistics. (1994). Annogl_§nmm_;y_of_oiroh§* mazzjaggg gjv vgzggal goo dooghg; Unigod State e5, 1923. Hyattsville, MD. U. S. Department of Health and Human Services, Public Health Service. National Research Council. (1991). Wrens. Washington, DC: National Academy Press. Novelli, W.D. (1984). Developing marketing programs. In L.W. Frederickson, L. J. Solomon, and K. A. Brehony (Eds. ), Morkooing healthJiehaxiermrrineieleaatechmaueeJnLannlieatiens. New York: Plenum. O'Brien, D.J., Hassinger, E.W., Brown, R.B., & Pinkerton, J.R. (1987). The social networks of leaders in more and less viable rural communities. Enzoi_fiooioiogy, 56(4), 699-716. Oliver, C. (1990). Determinants of interorganizational relationships: Integration and future directions. Aoodomy_o£_nonogomono_3oxioo, 15(2), 241-255. Papa, M.J., & Tracy, K. (1988). Communicative indices of employee performance with new technology. Connnniootion_8oooaroh, 15, 524- 544. Parsons. J.S. (1973). InteraetienLanLcemmmieatimmzhilienine - . Unpublished doctoral dissertation. Honolulu, University of Hawaii. Parsons, T. (1956). Suggestions for a sociological approach to the theory of organizations. AdministratixLSeieneeflrarterlx. 1. 63- 69, 74-80. Pfeffer, J. (1981). Some consequences of organizational demography: Potential impacts of an ageing work force on formal organizations. In S.B. Kiesler, J.N. Morgan, & V.K. Oppenheimer (Eds.), Aging; fiooioi_ohongo, 291-321. New York: Academic Press. Pfeffer, J. (1983). Organizational demography. In L.L. Cummings & B.M. Staw (Eds.). Researeh_in_arganizatienal_hehaxier. Vol. 5. 299-357. Greenwich, CT: JAI Press. Powell, R.M. (1952). Sociometric analysis of informal groups--their structure and function in two contrasting communities. fiooiomoory, 15, 367-399. Provan, K.G. (1983). The federation as an interorganizational linkage network. WW. 8(1). 79-89. Provan, K.G. (1984). Interorganizational cooperation and decision making autonomy in a consortium multihospital system. Aoooony_o£ ManagemenLBeeiee. 9(3). 494-504. 175 Przeworski, A., & Teune, H. (1970). The logio of comparative social inooizy. New York: John Wiley. Reid, W.J. Inter-organizational coordination in social welfare: A theoretical approach to analysis and intervention. In R.M. Kramer 6- H. Spect (Eds.). ReadingLimmmunitLerganizatioLnractiee. Englewood Cliffs, NJ: Prentice-Hall. Roberts, R.H., & O'Reilly III, C.A. (1979). Some correlates of communication roles in organizations. Aoadomy_o£_flonogomon; Journal. 22(1). 42-57. Rogers. E.M- (1983). DiffusioneLinnoxatienemmEditienl. New York: Free Press. Rogers, E.M., & Bhowmik, D.K. (1971). Homophily-heterophily: Relational concepts for communication research. Enoiio_Qoinion_Qnonooziy, 34, 523-538. Rogers. E.M.. & Kincaid. DL. (1981). CemmieatimnetmrkaJexarrLa Waugh. New York: Free Press. RogerS. B.M.. 6: Shoemaker. F.F. (1971). CemmrnieatienJLinnexatienLA ereaaemrlturautnreaeh. New York: Free Press. Rogers, E.M., & Storey, D. (1987). Communication campaigns. In C. Berger & 8- Chaffee (Eds.). W. Newbury Park, CA: Sage. Sarason. S.B.. 6: LorentZ. E. (1979). IhLehallengeeLtereaanree oxohongo_no§xork. San Francisco, CA: Josey-Bass. Schermerhorn, J.R., Jr. (1975). Determinants of interorganizational c00peration. Academy—ofianasementionrnal. 18. 846-856. Schopler, J.H. (1987). Interorganizational groups: Origins, structure, and outcomes. AsademLQLManasemenLRexiew. 12(4). 702-713. Schwebel, A.I., Kershaw, R., Reeve, 8., Hartung, J.C., & Reeve, W. (1973). A community organization approach to implementation of comprehensive health planning- AmericanieurnalJLanflealth. 63, 675-680. SPSS. Inc. (1993). W. v. 4.0.1. [computer program]. Chicago, IL. Stephen, F. (1952). The relative rate of communication between members of small groups. Americanjeeielegiealjexieu. 17. 482-486. Thibaut, J. (1950). An experimental study of the cohesiveness of underprivileged groups. Honan_3oiooiono, 3, 251-278. 176 Thomas, S.B. (1990). Community health advocacy for racial and ethnic minorities in the United States: Issues and challenges for health education. Health_Edueatien_Quarterlx. 17(1). 13-19. Thompson, B., & Kinne, S. (1990). Social change theory: Applications to community health. In N. Bracht (Ed.), Hoalth promotion a; tho oommnni;y_loxoi. Newbury Park, CA: Sage. Tushman, M.L. (1978). Technical communication in R&D laboratories: The impact of project work characteristics. Agogony_ofi_nonogomon; Journal, 21, 624-644. U.S. Department of Health and Human Services. (1990). Hoolohy_ooooio 000' \:_ es, Q‘; q 0 one so :qe e_ ._ o - ‘9 e. co Washington: U.S. Department of Health and Human Services. Valdiserri, R.O., Aultman, T.V., & Curran, J.W. (1995). Community planning: A national strategy to improve HIV prevention programs. Ieurnal.ef.§eumunitx_flealth. 20(2). 87-100. Van Beurden, E., Lefebvre, R.C., & James, R. (1991). Transferring community-based interventions to new settings: A case study in heart health cholesterol testing from urban USA to rural Australia. Health_£remetien_lnternational. 6(3). 181-190. Van de Ven, A.H. (1976). On the nature, formation and maintenance of relations among organizations. Aoodony_of_Monogonon§_Roxioo, 1, 24-36. Walker, R. (1992). Inter-organizational linkages as mediating structures in community health. Health_£remetien_1nternatienal. 7. 257-264. Warren, R. (1967). The interorganizational field as a focus for investigation. Adminiatratixe_§eienee_Quarterlx. 12. 396-419. Weick, K. (1976). Educational organizations as loosely coupled systems. Adminiatratixe_&eienee_9uarterlx. 21. 1-19. Wheeler, L., Nezlek, J. (1977). Sex differences in social participation. Iaurnal_ef_Peraanalitx_and_§eeial_£axehelegx. 10. 742-754. Whetten, D. (1981). Interorganizational relations: A review of the field. Jeurnal_ef_flisher_Edueatien. 52. 1-28. Wilensky, H.L. (1967). Qzgonizo;ionoi_intoiiigonoo. New York: Basic Books. Windsor, R.A., Baranowski, T., Clark, N., & Cutter, G. (1984). . Mountain View, CA: Mayfield. 177 Winett, R.A., King, A.C., & Altman, D.G. (1989). Hooi;h_ooyohoiogy_ond W. New York: Pergamon. Wisconsin AIDS/HIV Program. (1995). Hiooonoin_AlD§zfliy_nodooo. Madison, WI. Wisconsin HIV Prevention Community Planning Council. (1994). Hiooonoin mareherraiXLliIV—nrexentienJlanJm. Madison. WI. WordPerfect Corporation. (1993). Eo;o_£zoooooing_£ookogo, v. 6.0. [computer program]. Orem, UT. World Health Organization. (1986). The Ottawa charter for health promotion. Healthlremtien. 1. iii-v. Yum, J.O. (1983). Social network patterns of five ethnic groups in Hawaii. In R.N. Bostrom (Ed.), Qonnnniootion_yooroook_1. Beverly Hills, CA: Sage. Zenger, T.R., & Lawrence, B.S. (1989). Organizational demography: The differential effects of age and tenure distributions on technical communication. W1. 32(2). 353-376. MICHIGAN STATE UNIV. LIBRARIES VI l l ”ml W ill “I llllll Ill l W ll” INN l" "N“ l l" I ll 31293013882307