MSU BRONSI NG COLLECTION LENDING REQQLAIIONS LIBRAP' '§ 2 Q THE EFFECTS OF EXPOSURE TO A PAMPHLET ABOUT THE THERAPEUTIC PROCESS ON ATTENDANCE OF ADULT OUTPATIENTS AT AN URBAN COMMUNITY MENTAL HEALTH CENTER BY Patricia Calvert Forman A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Department of Counseling, Personnel Services and Educational Psychology 1983 Copyright 1983 Patricia Calvert Forman ABSTRACT THE EFFECTS OF EXPOSURE TO A PAMPHLET ABOUT THE THERAPEUTIC PROCESS ON ATTENDANCE OF ADULT OUTPATIENTS AT AN URBAN COMMUNITY MENTAL HEALTH CENTER BY Patricia Calvert Forman Purpose The primary purpose of this study was to determine whether more clients at a community mental health center would attend scheduled initial appointments if they were educated prior to the first interview about the therapy process and what was expected of them, using a pamphlet. Two secondary hypotheses were explored. First, that lower socioeconomic status clients would respond more favorably to the pamphlet than their higher socioeconomic status counterparts. Second, that clients who attended and those who did not could be differentiated on the basis of 25 descriptive variables. Methodology An eight column, two-sided pamphlet was designed and then reviewed by professional editors and clinicians prior to a pilot study. After revision, the pamphlet was mailed, Patricia Calvert Forman prior to the first interview to 389 adults who requested outpatient therapy from March 31, 1981 to September 18, 1981. Of those clients who attended the first interview, 257 at- tended the second. The data were subjected to two four-part analyses: log linear analyses and a discriminant function test of the dependent variable of attendance. Results The log linear analysis of the first and second inter- views yielded no significant differences between treatment and control groups. Socioeconomic status significantly differentiated attend versus nonattend and cancel versus nonattend categories at the .05 level. There was a sex by treatment interaction for the second interview, cancel versus nonattend response categories. 'The discriminant function analysis yielded one signi— ficant function for the first session, attend versus non- attend. Attenders had a higher income and were more likely to be married and financially dependent than nonattenders. The results of the analyses suggested that a well— researched and constructed pamphlet, in itself, is not suf— ficient to motivate clients to attend or cancel their first or second appointments. The discriminant function analyses suggested that there may be clusters of variables which can differentiate between attenders and nonattenders. DEDICATION This thesis is dedicated to: My husband, Gregory D. Cook, whose humor, love and faith in me have made it all possible; My daughter, Kathryn Blaine, in the hope she will not have to strug— gle as I did to accept being a woman; My father and mother, Fred P. and Janet R. Forman, whose love and support started this whole process; and My advisor at SUNY Buffalo, Robert Rossberg, Ph.D. whose intellect and wit were a wonderful challenge. ii ACKNOWLEDGEMENTS I am grateful to the many people who made this re— search possible, but especially to the following: to Dr. William Farguhar who as an advisor, doctoral committee and dissertation chairman encouraged me to con- tinue with my research despite many interruptions; to Dr. Andrew Porter who as a teacher and a member of the doctoral committee has been consistently a source of energy; to Dr. Judith Taylor whose support and understanding has gone beyond her role as a doctoral committee member; to Dr. Sam Plyler whose thoughtful comments both within the doctoral committee and outside of it have been helpful; to Dr. James Engelkes who as a member of the doctoral committee offered assistance at an important time. to the staff at the Ingham and Mason Community Mental Health Centers for their cooperation and suggestions and particularly Mrs. Nancy Thelan, Mrs. Mariann Aldridge, Mrs. Margaret Lilly, Mrs. Kathy Hill and Mrs. Jackie Lawrence whose willingness to collect data went well beyond their jobs; and to all the clients who participated in this research, thank you. iii TAB LE OF CONTENTS CHAPTER PAGE CHAPTER ONE.. ................. . ...................... 1 Introduction .................................... l The Problem ..................................... 4 Purpose of the Study ............................ 7 Hypotheses.... .................................. 9 Overview ........................................ 10 CHAPTER TWO....... ................. . ................. ll Review of the Literature ........................ ll Premature Termination Studies ................... 12 The Setting ..................................... l4 Therapist Variables ............................. 17 Client Characteristics .......................... 19 Type of Therapy Offered .................. . ...... 25 Strategies to Improve Client Attendance ......... 27 Changing the Intake Process ..................... 27 Matching Clients With A Theoretical Orientation. 28 Educating the Therapist ......................... 30 Matching Client and Therapist Characteristics and/or Expectations ............................. 3l Educating the Client ............................ 32 Summary ......................................... 39 The Pamphlet..... ............................... 39 Theoretical Considerations ...................... 40 Technical Aspects ............................... 45 CHAPTER THREE.......... .............................. 56 Design of the Study ..... .. ...................... 56 Construction of the Pamphlet .................... 57 Selection of the Sample ......................... 60 Sample Size ........ . ........... . ................ 62 Procedure ....................................... 62 Informed Consent... .......... . .................. 65 Design of the Study ..... . ................... .... 78 Analysis ........................................ 85 Summary...... ............. . ..................... 92 iv CHAPTER PAGE CHAPTER FOUR.............. .......................... 93 Analysis of the Data.... ..... ... ..... ... ....... 93 Log Linear Analysis..... ....................... 93 The Multiqual Model................. ........... 102 Discriminant Function Analysis of the Descriptive Variables .......................... 106 Summary............. .......... ....... .......... 113 CHAPTER FIVE......... ..... .. ................. . ...... 116 Summary, Conclusions and Suggestions for Further Research.......................... ..... 116 Summary ........................................ 116 Conclusions ...... .................... .......... 120 Discussion and Implications for Further Research ....... . .......... . .................... 128 APPENDICES.. .......... . ............................. 133 BIBLIOGRPAHYOOOO.........OOOCOOOO..........OOOOOO... 177 LIST OF TABLES Attendance Rate for Ingham Community Mental Health Center Clients At The First Inter- view From April-September, 1979-1981..... ..... First and Second Appointment Attendance ....... Response Variables For The First and Second Interview For Clients Who Attended ............ Response Variables For the First and Second Interview For Clients Who Did Not Attend...... Income of Community Mental Health Clients..... Age of Clients...... ...... .................... Educational Level of C1ients..... ............. Insurance.......................... ........ ... Living Arrangement of Community Mental Health Clients................................ Summary in Percentages of First Appointment Attendance As A Function of Treatment, SES and Sex...................................... ..... Summary in Percentages of Second Appointment Attendance As A Function of Treatment, SES and Sex........................................... Client Attendance at the First Interview...... Client Attendance at the Second Interview ..... Research Design................ ........... .... Summary of the Analyses for the First and Second Interview......................... ..... Log Linear Analysis of the Response Variable Attendance As A Function of Treatment, SES and Sex in the First Interview................ Log Linear Analysis of the Response Variable Cancellation As A Function of Treatment, SES and Sex.......................... ....... ...... Log Linear Analysis of the Response Variable Attendance As A Function of Treatment, SES and Sex, Second Interview..................... Log Linear Analysis of the Response Variable Cancellation As A Function of Treatment, SES and Sex At The Second Interview......... ...... Chi Square Analysis of the Response Variable Cancellation As A Function of Treatment And Sex for the Second Interview.................. vi PAGE 63 67 68 69 73 74 76 78 79 80 81 82 83 85 87 96 97 98 99 103 Variables Included in the First and Second Session Discriminant Function Analyses.. ...... Summary Statistics for the First Interview Attendance Vs. Nonattendance Discriminant Function Analysis... .......................... Summary of the Discriminant Function Analyses....... ..... .......... ................ Summary of Hypotheses Tested... ......... . ...... vii PAGE 108 109 110 LIST OF FIGURES FIGURE PAGE 3.1 Sex of Community Mental Health Clients in Percentages.......................... ........ 71 3.2 Socio-economic Status and Sex of Community Mental Health Clients..................... ..... . 72 3.3 Marital Status............................ ...... 75 3.4 Employment Status............................... 77 4.1 Plot of Treatment by Sex Interaction at the Second Interview For Cancellations...... ...... ..103 viii CHAPTER ONE INTRODUCTION The existence of mentally ill individuals has always been problematic for society and solutions have run the gamut from banishment to tolerance, treatment and grudging acceptance. Cases of eccentricity, psychosis, symptoms of medical disorders (e.g., senility,epil€PtiC seizures, syphillis) and interpersonal conflicts of varying severity, have been lumped together and treated with a multiplicity of remedies, the choice frequently contingent on culture, sex, and social class. Historically, the most severe cases of mental illness, in which individuals lost touch with reality, whether as the result of psychological disorder or physical symptoms, were isolated from society. With in- creasing sophistication, societies no longer "banished" these luckless individuals altogether, but institutionalized them. Once in an institution, they received treatment that varied from warehousing and medication to electroshock. InStitutions grew as they came to be convenient "catch alls" for all those persons who were different from rigid commun- ity norms or were otherwise inconvenient to have around. Psychotropic drugs, however, developed in the 19005, have 1 significantly reduced the numbers of these seriously distur- bed people who need long—term institutional care. For less seriously disturbed persons, the historical solutions were also contingent on social class. With the advent of Sigmund Freud's psychoanalysis in Europe in the 19005, the wealthy upper and upper-middle classes found a palatable solution for the care of mentally ill family members. An individual could be treated without hospitali— zation and generally at home. Freud's premise was clear: if mentally ill individuals could understand what their symptoms represented on an unconscious level, they could, through analysis, gain control of their unconscious impulses and become healthy. Treatment included talking to analysts about anything that came into the patients' minds (free association) and analysts' interpretations of patients' unconscious motivations. For the less wealthy, lower-middle and lower classes, there was little time, money or inclination to try to ef- fect an analytic "therapy cure". Hollingshead and other re— searchers noted that, historically, the lower classes have consistently accounted for a higher percentage of seriously ill persons in mental institutions than the upper classes. Among the reasons that have been suggested are: less pre- vention, more stress, less tolerance for eccentric behavior, political and social persecution and, perhaps, more congenital 3 mental illness. To these reasons must be added a lack of money to pay for private community-based treatment and a dearth of such treatment on a public basis. As society became more egalitarian, however, the class distinctions eased, community-based mental health treatment was given higher priority. This treatment, radically changed from the early 1900's, now includes many different types of psychotherapy, chemotherapy, therapeutic activities, partial and complete hospitalization, and outpatient psychotherapy and chemotherapy, delivered to the public and funded partially or entirely by federal, state and local tax dollars. While personal wealth no longer dictates access to services or what kinds of services are available, some as- pects of treatment, such as the usual referral source and payee, are still related to socioeconomic status. Private psychiatrists, hospitals and clinics continue to serve up- per and upper-middle class patients, with some of these services available to middle and working class persons whose insurance covers private psychiatric care. Others in these groups go to community mental health (CMH) clinics where sliding fee scales are available if medical insurance is not. Referral sources are generally private physicians, family, friends or self. CMH centers are the primary pro— viders for lower-middle and lower socioeconomic status (SES) clients. These people are referred for treatment, not only by physicians, but by the courts and social service agencies. 4 CMH centers offer a variety of services ranging from individ- ual to family and group therapy, including referrals to and coordination with other community agencies. As public, community-based mental health centers have become more prevalent and more lower SES clients have used them, some problems have been noted that are not particularly visible with other groups. One important concern has been the poor attendance rate for appointments. Researchers have generated many hypotheses that focus on three main areas: (1) the agency characteristics, e.g., a large bureau- cracy may alienate clients, (2) the characteristics of the problem and the referral source, e.g., involuntary court referrals may generate more antagonism, (3) the clients' characteristics, e.g., the clients may lack information about therapy and be hesitant to arrive for appointments. Such questions about lower SES clients' failure to use com— .munity mental health center services more effectively than they have, have been investigated in research studies in many ways without consistent conclusions. Why does one out of three clients fail to attend an initial session? The need for answers to these questions points up a need for further research. The Problem As noted above, community mental health centers serve a predominantly lower class population. Within this group, 5 client problems span a continuum from those who have been discharged from psychiatric hospitals to those experiencing minor adjustment problems. The clients are referred by a number of sources; some request help on their own or are referred by their families, others are referred by physicians, the court system, or a social service agency. Some attend therapy voluntarily, others are mandated by the courts to attend. The expectations of these clients vary widely. In order to serve these clients as efficiently and appropriately as possible, it is important for the referral process to go smoothly. Given the diversity of client func- tioning levels, the nature of the referral source and client information about therapy, a smooth referral process is often not achieved. The first part of the therapy--making an appointment for the first session and attending--is often the most dif- ficult stage. It is certainly the most crucial. Nothing can be achieved without starting, but it is common in urban CMH centers for approximately 25 percent of clients who are re- ferred or request initial appointments not to keep them (Fiester, 1975). As many as another 37 to 45 percent of the outpatient population do not keep their second appointments (Fiester, 1975; Borghi, 1968; Heine, 1960; Imber, 1955). These at- tendance rates have not changed substantially over the last 20 years (Baekeland, 1975). For example, an average of 34.6 per— cento m.mo N.vm o.mo m.mo m.mm mccmuum mo mmmucmoumm M H.mo n x o.m~ m.om o.m~ o.mm m.Hm m.~m ucmuumm CH l ccmuumcoz + Hmucmo o.H> m.mo o.an o.mo m.mo m.no unmoumm CH ccmuu< ovm baa mma mma «Hm ova c Houoe omma m.am u M o.mm m.ae >.Nv o.Hm m.av v.om ucmoumm ca ccmuum :0: + Hwocmo o.¢o H.mm m.nm o.mo m.mm o.mm ucmoumm CH .ccmuuc. mma mma Noa moa med NNH c Hmuoe mhma “mnemummm umsmsc wand mcoo mm: Haumc Ham» amumpma .mmmzmeEmmnsHmma some zmHsmmezH EmmHm NEE B¢ mBZMHJU mmfizmu EBJ¢MI JBHZDZEOU 2.H~ m.v m.m>nx moan: mmm n 30 ca N em 2 a m.m>nx . . . I . I «mmduz o h hmuz Hhuz mum HN m.a m.m o.Hmux mmamz can: N N hauz mamuoe pcmuumcoz Hmocmu ficmuuc xmm ozc mmm .92m29mm<223m "Ha.m mam<9 82 TABLE 3.12: CLIENT ATTENDANCE AT THE FIRST INTERVIEW Number of Clients Percent Attend 251 64.5 Nonattend 75 19.3 Cancel 23 5.9 Cancel, Reschedule No Show 11 2.8 Cancel, Reschedule Show 29 7.5 Total 389 100.0 83 TABLE 3.13: CLIENT ATTENDANCE AT THE SECOND INTERVIEW Number of Clients Percent Show 158 56.6 No Show 28 10.0 Cancel 12 4.3 Cancelled, Rescheduled, and No Show 3 1.1 Cancelled, Rescheduled, Show 11 3.9 Not Rescheduledl 22 7.9 No Response2 33 11.8 Contact Withdrew3 12 4.3 Total 279 99.9 1Client and therapist decided a second session was unnecessary. 2Client was contacted by letter and did not respond. 3Client was contacted by phone or letter and did not wish to schedule another appointment. 84 For the purposes of the analysis, the hypotheses for the response variables for the first and second interview were collapsed: first, to those clients who attended and those who did not attend and second to those who attended, those who cancelled and those who did neither. An explana- tion of the collapsed categories was given earlier in this chapter. This strategy was developed for two reasons. First, it was likely there would be empty cells in the design un— less the response variables were collapsed which could af- fect the analysis and second, that if only two categories remained, potentially important information about inter- vention would be lost. Additional analyses was designed to describe those clients who were most likely to attend therapy sessions, using the descriptive variables cited earlier in this chap- ter. In addition to those demographic variables, several others were included based on hypotheses about client at- tendance. For example, if the client's sex matched the therapist's for the first interview or for the second, would that affect attendance? If the therapist was changed be- tween the first and the second interview, would that affect client attendance? Other variables which described the therapist were included: profeSsion, educational level. Finally, would the amount of time between the first and second appointment affect attendance? These variables and the descriptive variables were listed in hierarchical order 85 and submitted to a discriminant function test to classify clients with respect to attendance. Both of these aspects of the research will be discussed further in the statistical analyses section. The design is described in Table 3.14 below. TABLE 3.14: RESEARCH DESIGN Males Females High Low High Low SES SES SES SES lst R Xl Pamphlet 2nd lst R X2 Control 2nd There were a minimum of 20 subjects per cell and a total of 389 subjects in the sample. Analysis Because the response data was categorical, the test- ing of the hypotheses was done using a nonparametric proced- ure called logit linear analysis and computations were done using Multiqual, a program developed by Bock and Yates 86 (August, 1973). Because Multiqual tests the goodness of fit for a given model, as well as individual hypotheses, the working variables SES and SEX were included along with the treatment to find the model which was the best descrip— tor of the observed data. As indicated previously, the response variables were collapsed to two categories: at- tended and did not attend for the first analysis, then to two other categories: cancelled and did not attend for those who did not attend the first session. The same two analyses were run for those clients who attended a second interview. In addition to the hypotheses testing analysis, discriminant function was used to classify clients into attending or non attending categories for the first analy- sis and cancelled and non attending categories for the second analysis. The procedure was repeated for the second interview for a total of four analyses. To summarize there were four analysis of the data for each statistical procedures (See Table 3.15). Multiqual or Log Linear Analysis Multiqual is an educational statistical package de- veloped by Bock and Yates to "analyze qualitative data by means of log-linear models and maximum likelihood estimation." (1973, p.1) The program has a number of advantages which 87 TABLE 3.15: SUMMARY OF THE ANALYSES FOR THE FIRST AND SECOND INTERVIEW First attend, non-attend Interview cancel, non-attend Multiqual Second attend, non-attend Interview cancel, non—attend First attend, non-attend Interview cancel, non-attend Discriminant . Function Second attend, non-attend Interview cancel, non-attend will be discussed below. The primary advantage is that Multiqual analyzes "qualitative data within a framework that is similar to that of analysis of variance (e.g., the analysis yields interaction terms). (Baker, p.75) The uses of log linear analysis, a method of research designed to analyze categorical data proliferated in the late 19705 (Fienberg, 1980). Until the 19705, the most commonly used nonparametric statistic was the chi square. Categorical data was presented in contingency tables and when several variables were considered simultaneously (e.g., a 2x2x2 table) they were considered a multidimensional contingnecy table. These tables were usually analyzed using 88 various two dimensional marginal totals (e.g., two variables at a time). Fienberg pointed out three problems with this strategy. (1) confuses the marginal relationship between a pair of categorical variables with the re- lationship when other variables are present. (2) does not allow for the simultaneous exam- ination of these pairwise relationships. (3) ignores the possibility of three factor and higher order interactions among the vari- ables. (1980, p.1) Log-linear analysis of cross-classified data does not have the problems noted by Fienberg. The analysis simultaneously includes the response and explanatory variables and the in- teraction among the variables. Log linear analysis is designed to identify the structure underlying a set of categorical variables (Rey- nolds, 1977). Analysis of variables is designed, "to assess the effects of independent variables on a dependent variable and to partition overall variance" (Fienberg, 1980, p.3). Log linear analysis deals with categories or groups of ob- servations. The unit of analysis is not individual scores, as in an ANOVA, but "cell probabilities or functions of cell probabilities." (Reynolds, 1977, p.110) To further explain the similaries between the log linear model and ANOVA, an explanation from Baker is included in Appendix K. As indicated, log linear analysis is useful in two ways: to test the goodness of fit of a model and to test 89 treatment main effects. Baker indicates that there are two test statistics commonly calculated for log linear analy- sis: the maximum likelihood ratio statistic 2 , :5 observed (3 = 2 (Observed) log (m) and the Pearson X2 X2 = (observed - expected)2 expected In each instance the statistic represents a comparison of the expected and observed frequencies for one model compared with another. The G2 or maximum likelihood estimate is generally more appropriate than X2 because it is additive and can be partitioned into additive components, each providing an in- dependent test (Reynolds, 1977; Bishop, Fienberg and Holland, 1975; Baker, 1981). With large populations the statistics 2 are generally equal. Large values of G of X2 indicate a poor fit and small test statistics suggests a good fit. Sample Zeros There is considerable dispute about the importance of zeros in the sample space. Log linear analysis assumes that all cells can be filled. In reality, most designs have empty cells. Various strategies have been suggested 90 to reduce the number of empty cells, of which collapsing the data is one. Collapsing the variables is useful be- cause it eliminates empty cells, but may potentially obscure the important relationships among the data. The algorithms for determining collapsibility is found in Reynolds (1977), Bishop, Fienberg and Holland (1975), Fienberg (1980). Another solution is to "smooth" the data, which involves adjusting the observed frequencies (Reynolds, 1977)- Goodman (1968) recommends adding one-half to each cell and Fienberg (1980) reduces the degrees of freedom when there are random zeros (See Goodman and Fienberg for more detailed explanations of their procedures). The Multiqual program, as a logit linear analysis, accommodates zeros in the cells by defining a logit for each cell whether the observations are missing or not. Criteria for Accepting Models Reynolds (1977) stresses that accepting a model is not necessarily as direct as finding a nonsignificant X2 or MLE summary statistic and deciding the model "fits." The models may have equivalent summary statistics, but one includes more terms than the other. The better choice is the most parsimonious model, since the model will always fit (e.g., account for all of the observed data) if enough parameters are entered and the model saturated. The model 91 which accounts for the data with the least number of variables is the best. Unlike regression analysis, there is no term for the strength or magnitude of the relationship. Maximum likeli- hood estimation of X2 indicates the goodness of fit of a model to a set of data, but not the strength of the associa- tion. In regression analysis, there is a multiple correla- tion coefficient which tells "how much of the variation in the dependent variable is attributable to the independent variables." (Reynolds, p. 177) If that value is small, the independent variables have little ability to explain variance in the dependent variable. A Chi square or MLE only indicates whether the expected frequencies conform to the observed data, not how meaningful the data is. The decision whether the relationship is meaningful or not needs to be made on the basis of theory and examination of the data. Interpreting Multiqual Results The Multiqual program develops a series of contrasts, then, from the simplest to the most complex (most terms) to test a model which best fits the observed data. The first model is the grand mean and tests the model that says there are no differences between observed and expected frequencies. The analysis continues to test the goodness 92 of fit moving from the simplest to the most complex model by adding one explanatory variable at a time. For example, see the table provided by Baker (1981) in Appendix L for a further explanation. Summary In Chapter Three, the construction of the pamphlet, its contents, design and evaluation were reviewed. The population, the selection of the sample and a summary of the characteristics of clients included in the sample were presented. The design of the study and specific hypotheses and methods of analysis were discussed. In Chapter Four the results of the analysis will be presented and these will discussed in Chapter Five. CHAPTER FOUR ANALYSIS OF THE DATA Following data collection, the data were analyzed in two ways: (1) log linear analysis using Multiqual, a Fortran program develOped by Bock and Yates (August 1973) and (2) discriminant function analysis. Both analyses tested the hypothesis that no relationship existed between treatment main effects and the response variable of atten— dance at the first or second appointment. Each analysis, log-linear and discriminant function, was conducted twice for the first appointment and twice for the second. In each case, attendance was divided into two dichotomous categories: (1) those clients who attended the interview and those who did not attend and (2) those non attending clients who cancelled their appointments and those who did not. Log Linear Analysis Because log linear analysis is used primarily to fit a proposed model which includes treatment main effects (explanatory variables) and response variables, the follow- ing section is divided into two parts. First, each hypothesis 93 94 proposed in the study will be stated and the results of the analysis indicated. Significant interactions will be dis- cussed under the appropriate hypothesis and under hypothesis 6. Second, an overall picture of the model tested will be discussed. The primary hypothesis of the study was that there would be no differences in client attendance at the first appointment between those clients who received a pamphlet and those who did not. Stated in the null: H1: There will be no differences in atten— dance at the first appOintment between clients who received a pamphlet about therapy and those who do not. To test this hypothesis, 389 clients who requested services at Ingham Community Mental Health from 3/81 through 9/81 were randomly assigned to treatment and control groups stratified on the basis of their SES and sex. Treatment, SES and sex were entered in the log-linear analysis. The analysis was conducted twice, once with attendance defined as the dichotomous response variable (attend and not attend) and second (cancel and not attend). There were 109 subjects. The analysis failed to reject the null hypothesis at the .05 level for either the first or second response variable. In other words, the observed data fit a model in which all the cells have the same expected frequency and no additional information (sex, SES, treatment) was needed. The MLE chi squares and probabilities for the first 95 response variable are listed in Table 4.1 below. For the second response variable they are in Table 4.2. Both Pear- sonian and MLE chi squares are included. As indicated earlier, the MLE was chosen to test the hypothesis because if its greater accuracy. Component chi squares were not calcul- ated because the model fit at the first level. There were no significant interactions. The second hypothesis tested whether there was any relationship between treatment and attendance at the second appointment. Stated in the null: H2: There will be no difference in atten— dance at the second appOintment between clients who receive a pamphlet about therapy and those who do not. To test this hypothesis 257 clients who attended the first appointment and scheduled a second appointment were in- cluded in Multiqual analyses. The analyses failed to re- ject the null hypothesis at the .05 alpha level for either response variable, attend versus non attend or second can- cel versus non attend. See Tables 4.3 and 4.4 for the re- sults. There was an interaction between sex and treatment at the second interview in the cancellation group. Three males in the treatment group cancelled versus none in the control group. Because the three males were only 3.4 percent of all nonattenders, it is unlikely the interaction marked a treatment effect. 96 mNN. m66. N Nm6.m 6N6.6 660666 x 66666 666. 666. 6 666.6 666.6 660666 mum 666. 666. m 6ma.m 666.6 660666 x66 6N6. N66. 6 N66.m 66N.m 660666 6x 666. 6N6. m 666.N N66.N 660666 666 6 New .666. 666. N NmN. 66N. 660666 6x x mum Nm6. 666. 6 6NN. 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Tronlalk as think I! wrong and perhaps Ilsa.youwlseeadodoralongwllhi your lherapisl. The lherapb! doc'co' dries. Occasiomly your umépm may lhink meddne VII help. APPENDIX D 140 APPENDIX D MASON MENTAL HEALTH CENTER Mason Mental Health Center is a satellite community mental health center of Ingham Community Mental Health (ICMH) and is administered by the same Tri-County Community Mental Board. Mason serves a predominantly rural community in comparison to the more urban population served by ICMH. Initially, samples of clients of both Mason Mental Health and ICMH Centers were to be included in the study. Prior to combining the samples, they were compared on a series of chi squares and t-tests on the variables of primary inter- est (i.e., sex of clients, SES, age, marital status, first therapist's sex, income, previous contacts with community mental health, severity of problem, number of days from referral contact to first appointment and number of days from the first appointment to the second, diagnosis, edu- cation, employment status, sex of therapist assigned, living situation, referral source. See Tables A-l and A-2. Clients of Mason and ICMH Centers were significantly dif- ferent on 13 of the 18 variables. As a result of the analy- sis, the samples were not combined. The ICMH sample was used for the present research. The descriptive data for Mason clients are presented in the following tables. 141 TABLE A-l: SUMMARY OF THE CHI SQUARES ANALYSIS OF DIFFERENCES BETWEEN ICMH AND MMHC CLIENTS Degrees of Chi Variable Freedom Square Significance Sex of client 1 6.308 .0120* SES 1 13.628 .0002** Age 7 42.522 .0000** Marital Status 5 20.826 .0009** Education 7 35.267 .0000** Referral Source 17 24.328 .1108NS Diagnosis 4 2.925 .5704NS Employment Status 9 6.863 .6513NS Sex of therapist 1 4.488 .034l* Living arrangement 4 29.058 .0000** Therapist's educational level 1 4.182 .0409* Second therapist's sex 2 1.429 .4895NS Referral Source 5 11.869 .0366* * significance at .05 level ** significance at .01 level 142 . . MOO.N mmoNH mm.v.m NV comm—z .HQQM UCN 0.... *mmo vom mm N m>m . . . . 0 Mo oz omc H ma pa mo AN «mm emcocH . . mmh. bom.o no.HH mm comm: .uaam uma ou mzmmm omv No u m>m . . . . D mo 02 vam mam 6 mo Ha mmm emcocH . . mmo. How. ov.a mo comm: muomucou mzmva wow me a m90H>mum mvo. pom. mm.a mam emcmcH oom. m>.o mm.m~ om comm: mo «moo. mmv NH.m mum. o~.HH mm.am mum Emcmcu m No.mmva mm.mmmm ma.mm~.aa hv comm: oeoocH *HND. flv HM.NI . o . . O NH va mu 00mm mN mow w m mom Emc cH .macmflm ma umme uouum coflumfi>mo cmm: mmmmu .:.:.U . ammumucH B Uumccmum cumccmum mo .02 >umEflum mo mmanmfium> mmMBzmu IBJm<223m "NI< mdm<9 143 The Mason Mental Health Center data was collected from Apri113, 1982 through September 18, 1982 and included a total of 69 subjects, 12 males and 57 females. Fifty- nine were in the lower SES and ten in the upper SES cate- gory. The mean income was $11,255.15 and the mean age 26.59. Of the sample, 45.8 percent were presently married. The majority of the clients were self- or family-referred (60.6 percent). Most were working full-time (47.9 percent) or part-time (12.5 percent). For a sizable majority (62.3 percent) this was their first contact with community mental health. Over half (58.0 percent) of the clients had no insurance and 56 percent lived with family as either the primary income source or a dependent. The average wait from referral to the first appointment was 11.07 days. Clients then waited an average of 14.83 days to the second appointment. Attendance at the first session is summarized below: Table A-3: ATTENDANCE AT FIRST SESSION--MASON Category Frequency Percentage attend 42 60.9 non attend 14 20.3 cancel 7 10.1 cancel, reschedule and non attend l 1.4 cancel, reschedule and attend 5 7.2 TOTAL 69 100.0 144 The attendance for the second interview is summar- ized below: TABLE A-4: ATTENDANCE AT SECOND SESSION--MASON Category Frequency Percentage attend 28 62.2 non attend 5 ,11.1 cancel 3 6.7 cancel, reschedule and attend 1 2.2 not scheduled 7 15.0 no response 1 2.2 TOTAL 45 100.0 Analysis Chi squares analyses of treatment, SES, and sex main effects indicated no relationship with attendance. T-tests of income, age, previous contacts with community mental health and number of days to the first or second interview, also indicated no relationship with attendance- APPENDIX E 145 CLIENT INFORMATION Medicaid? Yee Ci . No Cl Don'tKnow Ci Medicaid Number: Labor Union Affiliation: NAME: ' Case 0: Reierrei om: ADDRESS: City: COUNTYITOWNSHIP: ZIP: PHONE PARENTS/GUARDIAN on spouse BIRTHDATE: CHILDREN OF CLIENT or SIBIJNGS OF CLIENT Name Blrthdete Name Blrthdete I Employer or School: Phone: ' Spouee’e Employer: Phone: if Child, Perente' F: Phone: Employeqe) M: Phone: Groee Annuei income: Insurance: Subecrlber'e Name: Group: BC: __ as: BCIBS: Contract: CURB Form 102 September 1970 Program Stamp Previous Mental Health Services: Dates Name of CiinlclHospltsl/Prlvate Therapist CItyIState Source of Referral: Reason tor Requesting Service: THERAPiST: Intake Date: Time: Fee: APPENDIX F 146 DMH-1810 (1/77) MICHIGAN DEPARTMENT OF MENTAL HEALTH COMMUNITY AGENCY STATISTICAL FACE SHEET 3 I I I 1. CASE NUMBER I I IIJIII' OPTIONAL (use with case no.) 2. COST CENTER CODE III I111 1 I 3. AGENCY (NUM BE R) I111II AGENCY (NAME) 4. OPENING DATE (MO.. 01.. Yr.) 5. UNIQUE IDENTIFIER I LI I 1 I I 1 I TION 0-D Initial LU U90". :7. UPDATE ORCORRECTISI: DaTEY', B.BIRTH DATE (Mo..Da.,Yr.) 9. SEX DE ICompIete II Action " V' - LQReODen 3.DCorrectionICode- l.or3. LI II 1 II 1 I I #I I 1 I I i I LDM 2.[:]F ll. COUNTY OF RESIDENCE 12.7CENSUS TRACT l3. GROSS INCOME (Annual) Code'L I J I l_| 4 L LI Code'I I I $0.33; DEPENDENTS 15.32511}: 1- [3 White 4. [3 Hispanic 16. SMTAARTIJSAL 1. C] Never Married 4. C] Separated y 3-5 13°17I_.1_I (Check One) 2. D Black 5. [:1 Asian (Check One) 2. CI Married 5- C] Divorced -12l 1 1 18+ I i I 3. D Am.lndian 6. [3 Other 3. D Widowed 6. [:1 Remarried EDUCATION I_i_I Highest Grade Completed Most recent special education program (check one) 5. D Emotionally Impaired 6. [3 Other Learning Disabilities 7. D Other NOT IN LABOR FORCE Years in special education I l . I 1. C] Educable (If blank skip to item 18.) 2. C] Trainable Currently in special education? 3. D Severely Mentally Impaired 1. I] Yes 2. C] No 4. I] Severely 3. Multiply Impaired EMPLOYMENT STATUS AT OPENING (Check One) CURRENTLY EMPLOYED UNEMPLOYED 1. [3 Full-time 4. [:I On lay-oft 8. E] Homemaker 2. CI Part-time 5. D Looking for work 9. D Child 3. D Sheltered Employment (Available during past 10. D Student 4 weeks) 11. I:] Never worked - 6. I: Not looking for work Non-student 12. C] 13. CI 14. [:1 Disabled Retired Other SOURCE OF REFERRAL. Code'I l I 20. PREVIOUS MENTAL HEALTH SERVICES (most recent - last 5 years) 3. D Inpatientlall other) 4. I] Other 1. C] None 2. D DMH Inpatient (Last 30 days) 0. D Unknown 21. PPB OBJECTIVE (Check One) 1.0 2.I:I 3.CI 4.[:] Crisis Resolution Maintenance Psychosocial Adjustment Rehabilitation/Habilitation . CURRENT LIVING 23. CLIENTS ELIGIBLE FOR PURCHASE OF SOCIAL SERVICES (Title XX) ARRANGEMENT Current Assistance Recipient Program Code I____I (From 055 M.A. Authorization Card) Code' L4_I Mo. Yr. Income Eligible - Scale 1. D 2. CI (Irom 055 form 1928) Eligible Beginning I I I I | I . DIAGNOSIS 25. OPTIONAL USE Primary Code | I I . I I Secondary Code I I I I I I I I I I I STATUS AT TIME OF CLOSING . HOW TERMINATED 1. CI Withdrew 2. CI According to Plan 3. C] Other 37. REFERRED TO Code' I l I 38. DATE OF CLOSING IMO.. Dav. Yr.) I L I L L I I 1 I {MARKS APPENDIX G 147 APPENDIX G THE TWO—FACTOR INDEX OF SOCIAL POSITION August B. Hollingshead 1965 Introduction The Two-Factor Index of Social Position was developed to meet the need for an Objective, easily appicable procedure to estimate the positions individuals occupy in the status structure of our society. Its develop- ment was dependent both upon detailed knowledge of the social structure, and procedures social scientists have used to delineate class position. It is premised upon three assumptions: (1) the existence of a status structure in the society; (2) positions in this struc- ture are determined mainly by a few commonly accepted symbolic characteristics; and (3) the characteristics symbolic of status may be scaled and combined by the use of statistical procedures so that a researcher can quickly, reliably and meaningfully stratify the popula- tion under study. Occupation and education are the two factors utilized to determine social position. Occupation is presumed to reflect the skill and power individuals possess as they perform the many maintenance functions in the society. Education is believed to reflect not only knowledge, but also cultural tastes. The proper com- bination of these factors by the use of statistical II. 148 techniques enable a researcher to determine within approximate limits the social position an individual occupies in the status structure of our society. The Scale Scores To determine the social position of an individual or of a household two items are essential: (1) the precise occupational role of the head of the house- hold performs in the economy; and (2) the amount of formal schooling he has received. Each of these factors are then scaled according to the following system of scores . A. The Occupational Scale 1. Higher Executives, Proprietors of Large Concerns, and Major Professionals a. Higher Executives Bank Presidents; Vice- Military, Commissioned Officers, Presidents; Judges Major and above, Officials (Superior Courts) 'Of the Executive Branch of Large Business, e.g., Government, Directors, Presidents, Federal, State, Local, Vice-Presidents, Assis- e.g., Mayor, City Manager, tant Vice—Presidents, City Plan Director, In- Executive Secretary, ternal Revenue Directors Treasurer. Research Directors, Large Firms 1 b. Large Proprietors (Value over $100,000 ) Brokers Dairy Owners Contracts Lumber Dealers 149 c. Major Professionals Accountants (C.P.A.) Economists Actuaries Engineers (College Grad.) Agronomists Foresters Architects Geologists Artists, Portrait Lawyers Astronomers Metallurgists Auditors Physicians Bacteriologists Physicists, Research Chemical Engineers Psychologists, Practicing Chemists Symphony Conductor Clergyman (Professionally Trained) Teachers, University, College Dentists Veterinarians (Veterinary Surgeons) 2. Business Managers, Proprietors of Medium Sized Businesses, and Lesser Professionals. a. Business Managers in Large Concerns Advertising Directors Office Managers Branch Managers Personnel Managers Brokerage Salesmen Police Chief; Sheriff District Managers Postmaster Executive Assistants Production Managers Executive Managers, Sales Engineers Govt. Officials, minor, Sales Managers, National e.g., Internal Revenue Concerns Agents Sales Managers (Over Farm Managers $100,000) b. Proprietors Of Medium Businesses (Values 535,000-51004000) Advertising Owners Manufacturer's Representatives ($100,000) Poultry Business ($100,000) Clothing Store Owners Purchasing Managers ($100,000) Real Estate Brokers Express Company Owners ($100,000) ($100,000) Rug Business ($100,000) Fruits, Wholesale Store Owners ($100,000) ($100,000) Theater Owners ($100,000) Furniture Business ($100,000) Jewelers ($100,000) Labor Relations Consultants 1The value of businesses is based upon the rating of financial strength in Dun and Bradstreet's Manual. 150 c. Lesser Professionals Accountants (Not C.P.A.) Military, Commissioned Officers, Chiropodists Lts., Captains Chiropractors Musicians (Symphony Orchestra) Correction Officers Nurses Director of Community Opticians House Pharmacists Engineers (Not College Public Health Officers (M.P.H.) Grad.) Research Assistants, University (Full-time) Social Workers Teachers (Elementary and High) Finance Writers Health Educators Librarians Administrative Personnel, Small Independent Businesses, and Minor Professionals a. Administrative Personnel Section Heads, Federal, State, and Local Government Offices Section Heads, Large Businesses Credit Managers and Industries Insurance Agents Service Managers Managers, Department Stores Shop Managers Passenger, Dept. Stores Store Managers (Chain) Private Secretaries Traffic Managers Purchasing Agents Sales Representatives Adjusters, Insurance Advertising Agents Chief Clerks b. Small Business Owners ($6,000-$35,000) Art Gallery Auto Accessories Cigarette Machines Cleaning Shops Awnings Clothing Bakery Coal Businesses Beauty Shop Convalescent Homes Boatyard Decorating Brokerage, Insurance Dog Supplies Car Dealers Dry Goods Cattle Dealers Feed Finance Co., Local Fire Extinguishers 5 & 10 Florist Food Equipment Food Products Foundry Funeral Directors Furniture Garage Electrical Contractors Engraving Business Monuments Package Store (liquor) Painting‘Contracting Plumbing Poultry Producers Publicity & Public Relations Real Estate Records and Radios Restaurant Roofing Contractor 151 b. Small Business Owners (Continued) Gas‘Station Glassware Grocery-General Hotel Proprietors Inst. of Music Jewelry Machinery Brokers Manufacturing c. Semi—Professionals Actors and Showmen Army M/Sgt; Navy C.P.O. Artists, Commercial Appraisers (Estimators) Clergymen (Not Profession- ally trained) Concern Managers Deputy Sheriffs Dispatchers, R.R. Train I.B.M. Programmers Interior Decorators Interpreters, Court Laboratory Assistants Landscape Planners d. Farmers. Shoe Shoe Repairs Signs Tavern Taxi Company Tire Shop Trucking Trucks and Tractors Upholstery Wholesale Outlets Window Shades Morticians Oral Hygienists Photographers Physio-therapists Piano Teachers Radio, T.V. Announcers Reporters, Court Reporters, Newspaper Surveyors Title Searchers Tool Designers Travel Agents Yard Masters, R.R. Farm Owners (325,000-535,000) Clerical and Sales Workers, Technicians, and Owners of Little Businesses (value under $6,000) a. Clerical and Sales Workers Bank Clerks and Tellers Bill Collectors Bookkeepers Business Machine Operators Offices Claims Examiners Clerical or Stenographic Conductors, R.R. Employment Interviewers Factory Storekeeper Factory Supervisor Post Office Clerks Route Managers (Salesmen) Sales Clerks Shipping Clerks Supervisors, Utilities, Toll Station Supervisors Warehouse Clerks Factories 152 b. Technicians Camp Counselors Dental Technicians Draftsmen . Driving Teachers Expeditor, Factory Experimental Tester Instructors, Telephone Co., Factory Inspectors, Weights, Sani— tary Inspectors, R.R. Factory Investigators Laboratory Technicians Locomotive Engineers Operators, P.B.X. Proofreaders Safety Supervisors Supervisors of Maintenance Technical Assistants Telephone CO. Supervisors Timekeepers Tower Operators, Truck Dispatchers Window Trimmers (Store) R.R. c. Owners of Little Businesses Flower Shop ($3,000-S6,000) Newsstand ($3,000-S6,000) Tailor Shop ($3,000—S6,000) d. Farmers Owners (510,000-520,000) Skilled Manual Employees Adjusters, Typewriter Auto Body Repairers Bakers Barbers Blacksmiths Bookbinders Boilermakers Brakemen, R.R. Brewers Bulldozer Operators Butchers Cabinet Makers Carpenters Casters (Founders) Cement Finishers Cheese Makers Chefs Compositors Diemakers Diesel Engine Repair & Maintenance (Trained) Diesel Shovel Operators Electricians Glassblowers Glaziers Gunsmiths Gauge Makers Hair Stylists Heat Treaters Horticulturists Lineman, Utility Linoleum Layers (Trained) Linotype Operators Lithographers Locksmiths Loom Fixers Lumberjacks Machinists (Trained) Maintenance Foreman Installers, Electrical Appliance Masons Masseurs Mechanics (Trained) Millwrights Moulders (Trained) Painters 153. Skilled Manual Employees (Cont) Electrotypists Engravers Exterminators Fitters, Gas, Steam Fireman, City Fireman, R.R. Foremen, Construction,Dairy Gardeners, Landscape (Trained) Printers Radio, T.V., Maintenance Repairmen, Home Appliances Riggers Rope Splicers Sheetmetal Workers (Trained) Shipsmiths Shoe Repairmen (Trained) Stationary Engineers (Licensed) Stewards, Club Switchmen, R.R. Small Farmers Owners (under $10,000) Paperhangers Patrolmen, R.R. Pattern and Model Makers Piano Builders Piano Tuners Plumbers Policemen, City Postmen Tailors (Trained) Teletype Operators Toolmakers Track Supervisors, R.R. Tractor-Trailer Trans. Typographers Upholsterers (Trained) Watchmakers Weavers Welders Yard Supervisors, R.R. Tenants who own farm equipment Machine OperatOrs and Semi-Skilled Employees Aides, Hopsital Apprentices, Electricians, Printers, Steamfitters, Toolmakers Assembly Line Workers Bartenders Bingo Tenders Building Superintendents (Cust,) Bus Drivers Checkers Clay Cutters Coin Machine Fillers Cooks, Short Order Delivery Men Dressmakers, Machine Drill Press Operators Photostate Machine Operators Practical Nurses Pressers, Clothing Pump Operators Receivers and Checkers Roofers Set-up Men, Factories Shapers Signalmen, R.R. Solderers, Factory Sprayers, Paint Steelworkers (not skilled) Stranders, Wire Machines Strippers, Rubber Factory Taxi Drivers Testers Timers Machine Operators and Semi-Skilled Employees Duplicator Machine Operators Elevator Operators Enlisted Men, Military Services Filers, Benders, Buffers Foundry Workers Garage and Gas Station Assistants Greenhouse Workers Guards, Doorkeepers, Watchmen Hairdressers Housekeepers Meat Cutters and Packers Meter Readers Operators, Factory Machines Oiler, R.R. Paper Rolling Machine Operators Farmers Tire Moulders Trainmen, R.R. Truck Drivers, General Waiters—Waitresses ("Better Places") Weighers Welders, Spot Winders, Machine Wine Bottlers Wood Workers, Machine Wrappers, Stores and Factories Smaller Tenants who own little equipment. Unskilled Employees Amusement Park Workers (Bowling Alleys, Pool Rooms) Ash Removers Attendants, Parking Lots Cafeteria Workers Car Cleaners, R.R. Car Helpers, R.R. Carriers, Coal Countermen Dairy Workers Deck Hands Domestics Farm Helpers Fishermen (Clam Diggers) Freight Handlers Garbage Collectors Grave Diggers Hod Carriers Hog Killers Hospital Workers, Ens ecified Host ers, R.R. Janitors, Sweepers Laborers, Construction Laborers, Unspecified Laundry Workers Messengers Platform Men, R.R. Peddlers Porters Roofer's Helpers Shirt Folders Shoe Shiners Sorters, Rag and Salvage Stagehands Stevedores Stock Handlers Street Cleaners Unskilled Factory Workers Truckmen, R.R. Waitresses--"Hash Houses" Window Cleaners Woodchoppers Relief, Public, Private Unemployed (NO Occupation)“ 155 Farmers Share Croppers This scale is premised upon the assumption that occupa- tions have different values attached to them by the members of our society. The hierarchy ranges from the low evalua- tion of unskilled physical labor toward the more prestige— ful use of skill, through the creative talents of ideas, and the manipulation of men. The ranking Of occupational functions implies that some men exercise control over the occupational pursuits of other men. Normally, a person who possesses highly trained skills has control over several other people. This is exemplified in a highly developed form by an executive in a large business enterprise who may be responsible for decisions affecting thousands Of employees. B. The Educational Scale The educational scale is premised upon the assumption that men and women who possess similar educations will tend to have similar tastes and similar attitudes, and they will also tend to exhibit behavior patterns. The educational scale is divided into seven positions: (1) Graduate Pro- fessional Training (Persons who complete a recognized pro- fessional course leading to a graduate degree are given scores of l). (2) Standard College or University Graduation. (All individuals who complete a four-year college or university 156 course leading to a recognized college degree are assigned the same scores. NO differentiation is made between state universities, or private colleges.) (3) Partial College Training. (Individuals who complete at least one year but not a full college course are assigned this position. Most individuals in this category complete from one to three years of college.) (4) High School Graduates. (All secondary school graduates whether from a private preparatory school, a public high school, a trade school, or a parochial high school, are assigned the same scale value.) (5) Partial High School. (Individuals who complete the tenth or the eleventh grades, but do not complete high school are given this score.) (6) Junior High School. (Individuals who complete the seventh grade through the ninth grade are given this position.) (7) Less Than Seven Years of School. (Individuals who do not complete the seventh grade are given the same scores irrespective of the amount of education they receive.) III. Intregration of Two Factors The factors of Occupation and Education are combined by weighing the individual scores Obtained from the scale positions. The weights for each factor were determined by multiple correlation techniques. The weight for each factor is: 157 Factor Factor Weight Occupation - 7 Education 4 To calculate the Index Of Social Position score for an individual the scale value for Occupation is multipled by the factor weight for Occupation, and the scale value for Education. For example, John Smith is the manager of a chain supermarket. He completed high school and one year of business college. His Index of Social Position score is computed as follows: Factor Scale Score Factor Weight Score X Weight Occupation 3 7 21 Education 3 4 ‘_12_ Index of Social Position Score 33 IV. Index of Social Position Scores The Two Factor Index of Social Position Scores may be arranged on a continuum, or divided into groups of scores. The range of scores on a continuum is from a low Of 11 to a high of 77. For some purposes a researcher may desire to work with a continuum of scores. For other purposes he may desire to break the continuum into a hierarchy Of score groups. I have found the most meaningful breaks for the purpose of predicting the social class position of an individual or of a nuclear family is as follows: 158 Social Class Range of Computed Scores I 11-17 II ' 18-27 III 28-43 IV 44-60 V 61-70 When the Two Factor Index of Social Position is re- lied upon to determine class status, differences in individ- ual scores within a specified range are ignored, and the scores within the range are treated as a unit. This pro- cedure assumes there are meaningful differences between the score groups. Individuals and nuclear families:with scores that fall into a given segment of the range of scores assigned to a particular class are presumed to belong to the class the Two Factor Index of Social Position score predicts for it. The assumption of a meaningful correspondence between an estimated class position of individuals and their social behavior has been validated by the use of factor analysis.2 The validation study demonstrated the existence of classes when mass communication data are used as criteria of social behavior. 2See August B. Hollingshead and Frederick C. Redlich, Social Class and Mental Illness, John Wiley and Sons, New York, 1958, pp. 398—4071 APPENDIX H Ezwm quzmz wuoe pm: Umpcmuum 0:3 mucmamo u m pcmuum ou >meHH once one: muucoo Ho OOHHOQ ecu >9 Omuummmu mum: on: mucmeao u N pcmuum Ou >Hoxfia once one: 2:0 cua3 muomucou once pm: pm: on: mucmaao u H "meoz .m.z me u pcmuumcoc m> Hmocmui ammo. mvm mmm.v mcumum xuoz mmvo. wvm woa.q cofiumodvm mo mumm» memo. mew emm.e mean new: pmedmnmau .nadm cam a can memo. Ova mmv.¢ amuumwmu uncou memo. ovm 5mm.v Hmuuommu HmoHpme .cummcuco mmmo. mvm Nah.v mcumum Hmueum: Nvao. mum Hoa.o .udcm ch ob uma eoum m>mc mo Honecz vmoo. wvm mvv.m mmm .meumum m coflumcvm ecu ca cmpcaoce meammaum> mm.mo .aaooo. AH meo.mm mew u dampened: m> pcdpuni 3OH>uoucH pcm .m.z mm H pcmuumcoc m> amochI hmmo. ovm wmo.m chNumocpm memo» Nmmo. oem th.v Hmuummmu accou mmmo. ovm omN.m H::O\3 muomucoo mcmmpmc mo Homecz .maumum m coHumccm one ca popcaoce mmanmeum> mm.~w «Nome. OH mom.ma Nem u .pcmuumcoc m> pcmuumi pmAMHmmmHo .wflcon mp mumcvm 3oH>uoucH pma xauomu ecu Iuoo mmmmo mo ucooumm onumm OZ< BmMHm m0 mmedcze ZOHBUZDE Bzm09ma mo. men um academecoemr mommm. mmmmm.~a mvmmm. Above. Namvo. maonm.a M mmmme. vaov.ma Aheam. o vmmmm. mowmm.a Ocmuucicoz vhvmm. Namoo.ma vmmmm. vommo. mmvow. vvomo.a Ocmuuc ocexdoz «accumusmm acmeao\.dmae Immenmmmm deepen: r:2o\3 .ecoo mo memo» xmm memm pucou um>m .>mum mo .02 mv.om~m ooom. mmcov. awth. M oo.o~ah ooom. vmmmm. ooomm. pcmuueicoz mm.owmm ooom. macaw. thvm. pcmuu< meoocH ucmEummuB mmm ucOHHO mo xmm mmdm ZOHBUZDE BZmMBZH EmmHm “m14 mgmma mo. was an dampenedoemr vmomo.ma momom. vhmmo. mvooo. M hemmm.ma Hence. mawva. vowmo. ccmuu<1coz ~mmm~.ma mmmmm. hmmoo. mmano. pcouue «conmmm ecoaumocpm ch m umH com eamuummmm mo memo» pmedmumce mEmm bongo Hmuummmm uncou Hemam. ommaa. hmmem. moomm.mm M ommma. emmha. mecca. hummo.mm pcmuu<1coz omamm. mmmmc. mvhmm. >m~om.o~ pcmuue eamuummmm Hmo «xuoz epmauum: n.wmmm 0cm 0» umH lace: comwuuco com mcfixooa Haaam some m>mo mo .02 ooom. omnov. Hemmm. M omhom. omenm. vmmom. pcmuu BZmm92H onumm u mi< mqm<9 176 and "other” sources, clients who had the same therapist for the first and second session and those clients who were more educated were more likely to attend the second session. There was no change in the lack of significant results in the cancel versus nonattend category for the second interview. In each instance, these new data should be inter- preted cautiously. They are the result of hypotheses about the data after the initial analysis and have no foundation in theory or previous research. 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