The International Journal of Psychosocial Rehabilitation

Peer-led vs. Clinician-led Recovery-Oriented
 Groups: What Predicts Attendance by Veterans?


Kathryn A. Bottonari, PhD
Charlie Norwood Veterans Administration Medical Center & Medical College of Georgia

Mark R. Schultz, PhD
Center for Health Quality and Economic Research, Edith Nourse Rogers Memorial Hospital

Sandra G. Resnick, PhD
VISN 1 MIRECC & Yale University School of Medicine

Lisa Mueller, PhD
VISN 1 MIRREC & Edith Nourse Rogers Memorial Hospital

Jack Clark, PhD
Center for Health Quality and Economic Research, Edith Nourse Rogers Memorial Hospital &
Boston University School of Public Health

Dolly C. Sadow, PhD, ABPP

MedOptions

Susan V. Eisen, PhD

Center for Health Quality and Economic Research, Edith Nourse Rogers Memorial Hospital &
Boston University School of Public Health

 

Citation:

Bottonari K, Schultz M, Resnick S, Mueller L, Clark J, Shadow, DC, Eisen SV (2012).Peer-led 
vs. Clinician-led Recovery-Oriented Groups: What Predicts Attendance by Veterans?  

International Journal of Psychosocial Rehabilitation. Vol 16(2) 88-105

Correspondence:
Susan V. Eisen, PhD
Professor
Health Research Scientist
Center for Health Quality Outcomes and Economic Research (CHQOER)
Edith Nourse Rogers Memorial Veterans Hospital
200 Springs Road (152), Bedford, MA 01730.
Email: Susan.Eisen@va.gov

Author Note
This research was supported by VA grant IIR # D4464R from the Veterans Administration Rehabilitation Research & Development (RR&D) Service.  The work was conducted at the Bedford and West Haven Veterans Administration Medical Centers. The views expressed in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs. This manuscript is the result of work supported with resources and the use of facilities at the Bedford VA and support to the first author through a VA Health Services Research Postdoctoral Fellowship. 



Abstract
The Veterans Health Administration (VHA) is transforming its mental health services to incorporate a stronger focus on psychosocial rehabilitation and recovery. Peer-led groups may offer unique benefit in addition to conventional clinician-led services.  There is limited empirical evidence comparing attendance at peer-led services compared to clinician-led services among veterans.  Data were examined from the treatment arms (peer-led vs. clinician-led) of a 2-site, randomized study of recovery-oriented groups for veterans diagnosed with mental illness. Baseline self-report and interview data were examined as predictors of attendance over the course of the 12-week study, using negative binomial regressions. Results demonstrated modest attendance rates (3.8 + 4.3 groups out of 12 weeks), with no statistically significant difference in attendance or predictors of attendance between clinician-led vs. peer-led groups.  Age, education, AUDIT score, and site predicted attendance at the recovery-focused groups in a combined model. Future investigations are needed to examine factors to improve attendance.
Key Words: peer support, recovery model, veterans, attendance



Introduction
In an effort to improve mental health services for veterans, the Veterans Health Administration (VHA) is working to incorporate a stronger focus on psychosocial rehabilitation and recovery throughout its treatment centers (Uniformed Mental Health Services Handbook, 2008).  A recent review of VHA psychosocial rehabilitation services highlights the increase in recovery-oriented services, including local recovery coordinators, peer support services, peer support technicians, family services, Veterans Mental Health Council, Psychosocial Rehabilitation and Recovery Centers, Mental Health Intensive Case Management, Therapeutic and Supported Employment Services, and Skills training (Goldberg & Resnick, 2010).  Mental health ‘recovery’ focuses on reclaiming one’s life and minimizing the effect of illness on quality of life (Davidson & Roe, 2007).  Davidson and colleagues (2009) define recovery-oriented services as being person-centered, strengths-based, and collaborative.  Ideally, the mental health consumer, friends, family, and treatment providers work together towards recovery. 

Peer support is one important component of recovery-oriented services, particularly in the VHA (Barber, Rosenheck, Armstrong, & Resnick, 2008; Hebert, Rosenheck, Drebing, Young, & Armstrong, 2008).  Empirical research has found positive effects for a range of peer support programs including improvements in practical knowledge, empowerment, in symptoms and social functioning;  development of new coping skills; and enhancement of quality of life, with decreases in symptoms and feelings of isolation, and reduced psychiatric hospitalization rates (e.g., Barbic, Krupa, & Armstrong, 2009; Burti et al. 2005; Campbell, 2004; Cook et al., 2009; Davidson et al., 1999; Fukui et al., 2010; Paulson, Herinckx, Demmler, Clarke, Cutter, & Birecree, 1999; Sells, Davidson, Jewell, Falzer, & Rowe, 2006; Solomon & Draine, 2001; Sledge, Lawless, Sells, Wieland, O'Connell, & Davidson, 2011; Yanos, Primavera, Knight, 2001).  Other recovery-oriented services have also been found to be effective, including supported employment programs, supportive housing, assertive community treatment and mental health case management (Drake & Bond, 2008; O'Connell, Kasprow, & Rosenheck, 2010; Rapp & Goscha, 2004). 

However, the effectiveness of these recovery-oriented programs may depend on the level of participant attendance. Empirical examination of this question has been hampered by noted difficulty in adequately measuring variables of adherence and attendance within recovery-oriented, peer-led interventions (Falzer, 2011). However, several recent studies of peer support/peer-led interventions have noted greater improvement with increased attendance. Cook and colleagues (2009) reported that participants who attended six or more recovery-oriented group sessions showed greater improvement than those attending fewer sessions. Similarly, Starnino and colleagues (2010) reported positive effects of their WRAP intervention with at least 75% participation in the program.  Swarbrick and colleagues (2009) found a relationship between years of attendance and empowerment, though they could not determine the predictor of this relationship. As such, research is needed to understand predictors of attendance and whether these factors vary as a function of the type of intervention being offered.

Review of the literature reveals investigations of both conventional, clinical-led intervention and peer-led (particularly with a focus on substance use groups); however, there is a limitation in that most studies focus on one particular type of intervention (i.e., peer-led or clinician-led but not both).  Empirical investigations of self-help group attendance have previously identified that older age, being single, having better coping skills, strong social functioning, more formal education, living in an urban area, and more significant mental health and substance use problems all predicted longer time in self-help treatment (Jonikas, Kiosk, Grey, Hamilton, McNulty, & Cook, 2010; Luke, Roberts, & Rappaport, 1993; Yanos, Primavera, Knight, 2001). Furthermore, investigations of a specific form of peer support, substance-use focused self-help groups (i.e., NA/AA), have identified increased attendance among those who were older, unemployed, African-American background, more highly educated, not religious, less socially isolated, previous substance use treatment, higher self-efficacy regarding sobriety, more arrests, earlier age of onset of substance use disorder, history of physical or sexual abuse, living in supportive housing, and more acute substance abuse/mental health problems (Brown, O’Grady, Farrell, Flechner, & Nurco, 2001; DiNitto, Webb, Rubin, Morrison-Orton, & Wambach, 2001; Laudet, Magura, Cleland, Vogel, & Knight, 2003; Kelly, & Moos, 2003; Schneider, Burnette, & Timko, 2008; Weiss et al., 2000). Investigations of attendance at more conventional clinician-led mental health services have found provided conflicting evidence with several investigations indicating that no-show to one’s appointments and/or lower attendance was predicted by younger age, less formal education, lack of sick leave, personality disorder diagnosis, particularly low or high functioning, no previous mental health treatment, substance abuse, fearful attachment style, interpersonal hassles (Fenger, Mortensen, Poulsen, & Lau, 2011; Ilardi & Kaslow, 2009) whereas others have identified higher attendance as predicted by social isolation, personality disorder, higher formal education, and more acute mental health disorder (Centorrino et al, 2001). Of direct relevance to veteran populations, Resnick and Rosenheck (2008) reported improvements in level of personal empowerment and confidence as well as overall functioning among veterans who participated in Vet-to-Vet as compared to veterans who had not participated in the program.  Their 2010 investigation reported that almost 88% of their sample drawn from an observational study had participated in Vet-to-Vet prior to the study period and approximately 50% attended at least one session during the study period.  Predictors of attendance included attending Vet-to-Vet prior to study enrollment, ongoing participation in the concurrent day program following completion of the study, lower recovery orientation, lower activities of daily living and older age (Resnick & Rosenheck, 2010). 

The present study expanded on these findings by a) examining attendance in both peer- and clinician-led recovery-oriented groups in the context of a randomized study, b) examining differences in attendance between peer-led (Vet-to-Vet) and clinician-led groups, and c) identifying predictors of attendance in both peer-led and clinician-led groups.

Methods
Overview
This investigation is part of a randomized study of a recovery-oriented group intervention led by either peer facilitators (Vet-to-Vet; Resnick, Armstrong, Sperrazza, Harkness, & Rosenheck, 2004), or by clinicians.  The intervention supplemented participants’ individualized treatment plans, which they continued during the 3-month study period.  Participants completed a baseline assessment (including a structured diagnostic interview) at enrollment and a follow-up assessment at the end of the intervention (3-months).

Participants
Eligibility criteria included being English-speaking, adult (at least 18 years old), having a mental health diagnosis, and having received mental health services through the VHA in the last 12 months.  We enrolled a convenience sample of veterans receiving mental health services from one of two participating sites. This analysis includes 198 veterans who consented to the study and were randomized to either the peer-led or clinician-led recovery groups. 

Measures
Demographic Characteristics:  Age, gender, race, employment, disability status and housing arrangements were obtained by structured interview at enrollment in the study. 

Psychological variables. We measured pre-intervention levels of hope (Snyder Hope Scale; Hope; Snyder et al., 1991), personal empowerment (Rogers Empowerment Scale; EMP; Rogers, Chamberlin, Ellison, & Crean, 1997, social support (MOS Social Support Survey; MOS; Sherbourne & Stewart, 1991), recovery (Recovery Assessment Scale; RAS; Corrigan, Giffort, Rashid, Leary, & Okeke, 1999), and patient activation (Patient Activation Measure; PAM; Hibbard, Stockard, Mahoney, & Tusler, 2004). All of these measures have demonstrated reliability and validity and most have been used in previous investigations examining recovery-oriented groups (e.g., Cook et al., 2009).

Mental Health and Substance Abuse Status. The pre-intervention (baseline) assessment included the following self-report measures of mental health symptoms and problems as well as substance use: Behavior and Symptom Identification Scale (BASIS-24, Eisen, Normand, Belanger, Spiro, & Esch, 2004), Veterans Short Form of the SF-12 (VR-12) from which we derived the Physical (PCS) and Mental (MCS) Component Scores (Kazis et al., 2004), Alcohol Use Disorders Identification Test, (AUDIT, Saunders, Aasland, Babor, De La Fuente, & Grant, 2006); Drug Abuse Screening Test; (DAST, Cocco & Carey, 1998).  Each of these measures has demonstrated reliability and validity and has been used extensively with veterans for both research and clinical purposes.  In addition to these self-report measures we assessed lifetime psychiatric diagnoses using the mood, anxiety, psychotic, and substance use disorder modules from the Structured Clinical Interview for DSM-IV Diagnosis (SCID-I, First, Spitzer, Gibbon, & Williams, 1997), from which we created four dichotomous variables indicating presence or absence of diagnoses in each of these categories.

Treatment Utilization. Participants self-reported their VA and non-VA care in the 3 months prior to study enrollment, including inpatient and outpatient mental health treatment, transportation difficulties regarding getting to treatment, and prior utilization of peer support.

Attendance.  The dependent variable was the total number of study-related groups attended during the 3-month study period, ranging from 0-12.

Procedure
Sample Recruitment. Veterans were informed about the study via fliers posted in clinic waiting areas and presentations at community meetings within mental health programs at each site.  Veterans who expressed interest were given detailed information about the study by study personnel. All those who agreed to participate signed a written informed consent form approved by their VAMC’s Institutional Review Board (IRB).  After signing consent, the research interviewer informed the veteran which study arm s/he was randomized to, and told them when and where the group met.  Randomization was done separately by site to equalize randomization at each site.  Veterans were not paid for group attendance, but they were paid for up to $60 total for study assessments.  Attendance rosters were collected from the group leaders after every meeting.  Veterans were not considered ‘drop-outs’ unless they formally requested to be withdrawn from the study, which occurred in only two cases.  Rather, since attendance was the dependent (outcome) variable in this study, no attendance was considered a valid outcome. 

Sites. The study was conducted at two large VAMCs in the Northeast.  Veterans were recruited from several programs at Site 1, including a structured residential program, a day treatment program, an outpatient mental health clinic, a long-term inpatient unit, several community residential care (CRC) homes, and a state-run residence for veterans.  Recruitment at Site 2 was conducted at a VA Community Care Center, which provides a range of psychosocial, medical and educational services.  These two sites were selected for the study because they each have extensive, well-established recovery-oriented mental health services. 

Recovery Groups. The peer-led and clinician-led groups shared many features.  All  groups (i.e., Peer and Clinician at Site 1 and 2) met at least weekly for 45 minutes, were recovery-oriented, with a focus on psychosocial rehabilitation and community integration.  The peer-led groups used the Vet-to-Vet model.  They were led by two peer facilitators who received training regarding implementation of the Vet-to-Vet model.  In addition, they had weekly supervision and ongoing evaluation by both a professional and a peer supervisor.  Peer facilitators were other veterans in treatment for a mental illness.  As peer providers, they are encouraged to share their personal experiences during group meetings.  Peer facilitators were paid as independent contractors; thus they were not VA employees.  The peer-led (Vet-to-Vet) groups followed an established curriculum using recovery-focused written materials, such as the Recovery Workbook (Spaniol, Koehler, & Hutchinson, 1994), or Pathways to Recovery (Ridgway, McDiarmid, Davidson, Bayes & Ratzlaff, 2002).  Each meeting focused on a different workbook chapter introduced by the facilitators and following a read and discuss format in which facilitators and participants discuss the topic and share their experiences.  Examples of workbook chapters included managing life’s stresses, building personal support, setting personal goals, and recognizing strengths. Clinician-led groups were led by a master’s level mental health clinician.  Some of the clinician-led group leaders chose to use the same recovery-focused written materials as were used by the peer facilitators, although this was not required.  However, all clinician-led groups followed a recovery-oriented model focused on enhancing interpersonal and functional skills.  Although both Vet-to-Vet and clinician led groups were structured around recovery issues, they are not “manualized” therapies in which the precise contents of each session are documented in a manual; consequently, the content of each group could vary from week to week. 

Two major differences between the Vet-to-Vet and clinician-led groups were expectations regarding sharing/disclosure of personal information, and clinical training.  Peer leaders of the Vet-to-Vet groups were expected to share their own experiences with group participants, whereas clinician leaders were not.  Clinician group leaders have professional clinical training, whereas peer leaders have a much briefer training program, which is however, followed up with continued training and ongoing supervision.  Of course, peer leaders also have personal experience as both a veteran and a person with a serious mental illness.  

Site 1 had 4 clinician-led groups and 4 Vet-to-Vet groups which ran over a 2-year period, but ended when the last study participants completed their 3-month follow-up assessments because research grant funds used to pay the peer facilitators were depleted.  Site 2 had 1 rolling, clinician-led group and 5 rolling Vet-to-Vet groups over the course of the study.

Data Analysis
Data analyses were performed using SAS (SAS Institute Inc, 2008) and SPSS (SPSS Inc, 2009).  Self-report measures were scored using each measure’s published algorithms.  First, we computed descriptive statistics to examine attendance patterns for the sample as a whole and divided by study arm (clinician- vs. peer-led).  Second, we used negative binomial regression to assess association between type of group (peer- vs. clinician-led) and attendance.  Negative binomial regression is appropriate for this analysis because attendance is a count variable and because of the relatively large number of cases who did not attend any groups (Hilbe, 2007).  To identify predictors of attendance, and whether predictors might differ for peer- vs. clinician-led groups, we regressed attendance on each patient characteristic alone and in interaction with type of group (peer- vs. clinician-led). However, since none of the interactions with type of group were significant, we computed bivariate correlations to examine the relationship between participant characteristics and attendance (i.e., pearson correlations or point biserial correlations when one of the variables is binary).  Variables that were significant at p<.05 were entered into a negative binomial mixed-model regression to examine unique predictors of attendance. 

Results
Sample Characteristics
As expected in a population of veterans, the majority of the sample were males (92%).  Average age was 52 + 10 years; 53% had some post-high school education.  Approximately one-quarter (28%) of the participants were non-Caucasian and 10% were Hispanic/Latino.  Mood disorders, substance use disorders and post-traumatic stress disorder (PTSD) or other anxiety disorders were all highly prevalent, occurring in more than 50% of study participants.  There were no statistically significant differences between study arms (peer- vs. clinician-led) in demographic characteristics, diagnosis category, or any of the T1 self-report measures with the exception of self-reported drug use (Peer-led group reported significantly higher use than the Clinician-led group; F(1,197)=5.64, p=0.0185).  Table 1 presents the sample characteristics.

Table 1.
Participant Characteristics

 

%

M(SD)

Range

Demographics

 

 

 

Gender

Male

 

92%

 

 

Age (years)

 

52 + 10

 

Ethnicity

Hispanic/Latino

 

10%

 

 

Racec

American Indian

Asian

African American/Black

Pac Islander

Caucasian/white 

 

3%

0.5%

28.8%

0%

71.7%

 

 

Education

High School diploma or less

More than High School diploma

 

47%

53%

 

 

Relationship status

Married/partner

Separated

Divorced

Widowed

Never married

 

7%

11%

40%

5%

37%

 

 

Lives alone

15%

 

 

Has no source of social support

8%

 

 

Has independent housing

20%

 

 

Has paid job

14%

 

 

Receives disability

60%

 

 

 

 

 

 

Psychological  Characteristics

 

 

 

Empowermenta

 

80.5(7.56)

28-112

Hopea

 

23.43(3.76)

12-48

Patient activationa

 

36.85(9.43)

0-52

Recoverya

 

163.6 (20.9)

42-210

Social Supporta

 

58.8(18.3)

19-95

 

 

 

 

Mental Health/Substance Use Status and Diagnoses

 

 

 

Physical health (PCS, VR-12)a

 

43.14(13.19)

unbounded

Mental health (MCS, VR-12)a

 

42.92(13.88)

unbounded

Behavior and symptoms (BASIS-24)b

 

1.23(.666)

0-4

Alcohol Use (AUDIT)b

 

7.65(8.29)

0-40

Drug Use (DAST-10)b

 

2.82(3.62)

0-10

SCID-I  Diagnosis

Mood Disorder

PTSD/Other Anxiety Disorder

Psychotic Disorder

Substance Use Disorder

 

64%

51%

28%

64%

 

 

 

 

 

 

Treatment Use in 3 Months Before Study Entry

 

 

 

Outpatient medical care

81%

 

 

Emergency medical care

29%

 

 

Inpatient medical care

17%

 

 

Outpatient mental health care

88%

 

 

Emergency mental health care

34%

 

 

Inpatient mental health care

43%

 

 

Outpatient substance abuse treatment

28%

 

 

Substance abuse detoxification

15%

 

 

Inpatient substance abuse treatment

25%

 

 

Stayed overnight for treatment

48%

 

 

Problem getting to treatment (i.e., transportation problems)

Big Problem

Small Problem

 

 

8%

15%

 

 

Ever used mental health peer support services?

7%

 

 

Ever used addiction peer support services?

49%

 

 

Ever used other peer support services?

10%

 

 

Note. aHigher scores are better; bLower scores are better; cMultiple responses accepted

Attendance at Recovery-Oriented Groups
Across the full sample participants attended an average of 3.8 + 4.3 groups over the 12 week study period.  Approximately one-quarter (26.5%) of the sample did not attend any study-related groups.  There was no significant difference in attendance by type of group with veterans assigned to peer-led groups attending 3.8 + 4.6 groups and veterans assigned to clinician-led groups attending 3.8 + 3.9 groups.  Since there were no group differences, attendance is presented for both groups combined (Figure 1). 



Predictors of Attendance.

The following baseline variables were significantly positively correlated with attendance (p<.05): older age, higher education, better mental health (MCS), more Hope and Psychotic Disorder diagnosis.  The following variables were significantly negatively correlated with attendance: emergency and inpatient mental health care, outpatient, detoxification, and inpatient substance abuse treatment, AUDIT and DAST scores,  any inpatient treatment in the past 3 months, and transportation difficulties getting to treatment (Table 2). Site was also a significant predictor of attendance. 

The following significant predictors were entered into a negative binomial regression model: age, education, overnight treatment, difficulty getting to treatment, AUDIT, DAST, Hope, MCS, SCID psychotic disorder diagnosis and site.  Because of high correlations between the AUDIT, DAST and substance abuse treatment variables, as well as between inpatient and emergency treatment we excluded the substance abuse and inpatient/emergency treatment variables from the regression model to minimize the impact of multicollinearity.

In the final model only age, education, AUDIT score, and site remained as significant predictors of attendance (see Table 2).  Older age and more education predicted better attendance.  Higher levels of alcohol use assessed by the AUDIT predicted lower attendance.  In addition, attendance was higher at Site 1 than Site 2.
 

Table 2. Association between Variables and Group Attendance

 

Correlation with attendance

F-value from final model

Demographics

 

 

Gender

0.01

Age

0.23*

9.24**

Ethnicity

0.03

 

Race

0.12

 

Education

0.21*

8.64**

Relationship Status

0.06

 

Lives Alone

-0.10

 

Has no source of social support

0.03

 

Has independent housing

-0.03

 

Has Paid Job

0.03

 

Receives Disability Income

-0.13

 

 

 

 

Psychological  Characteristics

 

 

EMPa

0.02


HOPEa

0.19*

2.27

PAMa

0.03


RASa

0.02

 

MOSa

0.12

 

 

 

 

Mental Health/Substance Use Status and Diagnoses

PCSa

-0.03

MCSa

0.19*

0.17

BASIS-24b

-0.12

 

AUDIT b

-0.26*

4.04

DAST b

-0.28*

2.47

SCID-I  Diagnosis

Mood Disorder

PTSD/Other Anxiety Disorder

Psychotic Disorder

Substance Use Disorder


-0.13

-0.10

0.15*

-0.11

 

 

 

1.67

 

 

 

Treatment Use in 3 Months Before Study Entry

Outpatient medical care

0.09

 

Emergency medical care

-0.05

 

Inpatient medical care

-0.07

 

Outpatient mental health care

-0.07

 

Emergency mental health care

-0.20*

 

Inpatient mental health care

-0.27*

 

Outpatient substance abuse treatment

-0.16*

 

Substance abuse detoxification

-0.21*

 

Inpatient substance abuse treatment

-0.20*

 

Stayed overnight for treatment

-0.27*

0.20

Transportation problem getting to treatment

0.17*

0.83

Ever used mental health peer support services?

0.00

 

Ever used addiction peer support services?

-0.10

 

Ever used other peer support services?

0.07

 

 


 

Study Characteristic


 

Site

-0.39

19.19**

Note. *p<0.05, **p<0.01, HOPE=Snyder Hope Scale; EMP=Rogers Empowerment Scale; RAS=Recovery Assessment Scale; PAM=Patient Activation Measure; BASIS-24= 24-item Behavior and Symptom Identification Scale; PCS=Physical Component Score of VR-36; MCS=Mental Component Score of VR-36; AUDIT=Alcohol Use Disorders Identification Test; DAST=Drug Abuse Screening Test



Discussion
This study examined attendance and predictors of attendance in peer-led and clinician-led groups within the VHA.  Although attendance levels were modest for both types of groups they were higher than that reported by Resnick and Rosenheck (2010), in which only 51.5% went to a group during their study period (though 87% had attended at least one Vet-to-Vet meeting before study entry).  Our investigation of potential predictors of attendance identified only age, education, AUDIT score, and site as statistically significant.  Older age, lower AUDIT scores, and higher level of education was associated with increased attendance, which is consistent with findings from Resnick and Rosenheck’s (2008) quasi-experimental study and (2010) investigation of Vet-to-Vet attendance, as well as several other studies (DiNitto et al., 2001; Laudet et al., 2003; Luke et al., 1993; Ogrodniczuk, Piper, & Joyce, 2006; Yanos et al., 2001).

Though unexpected, site was a significant predictor of attendance, with veterans at Site 1 attending an average of 5.3 groups in comparison to 2.2 groups at Site 2.  There were several site differences (e.g., in sample characteristics, facilitators and clinical programs) that could account for this effect.  Examination of demographic and clinical characteristics between participants at the two sites showed differences in race (higher percentage African-American at Site 2 than Site 1), Hispanic ethnicity (higher percentage of Hispanic veterans at Site 2), gender (more females at Site 2), recent substance use (more substance use at Site 2), and service use in the previous 3 months (higher level of service usage at Site 2).  This last finding is of interest as Site 2 reported higher level of service usage but attended fewer groups.  This may be consistent with the findings of Zemore and Kaskutas (2008), who noted in their investigation of day-hospital and residential programs, that attendance varied at curricular vs. extra-curricular (non-prescribed groups) as a function of type/intensity of current treatment.   As AUDIT score varied between site and also predicted attendance, it may be that self-reported alcohol use partially accounts for the site difference in attendance.  However, there may be other program characteristics and unmeasured site characteristics which also partially account for the site differences. 

The fact that only four variables remained as significant predictors, raises questions as to what other factors might predict attendance.  Participant satisfaction with the groups, cultural sensitivity of offerings, utilization of other treatment modalities, number of concurrent groups being attended, and engagement in other structured activities (e.g., work) could also be examined as possible predictors of attendance.  Furthermore, there is some evidence from the addiction literature that research should be addressing level of participation, rather than attendance, as a predictor of outcome (Weiss et al., 2005).

Strengths and Limitations
There are a number of strengths to this investigation.  First, the previous literature has been vague about participation rates in recovery-oriented services and our study design allowed us to systematically track and report attendance.  Second, although there is a growing body of literature on peer support services, this is the first randomized study examining peer-led versus clinician-led recovery-oriented groups for veterans with a mental illness.  Third, our study measured several patient-level factors, including demographics, psychological factors, mental health status, and treatment utilization variables, allowing us to investigate a wide range of possible predictors of attendance.

This study also has limitations.  First, there is an inherent paradox between the idea of a randomized clinical trial in which participants agree to attend whatever intervention is part of the randomized trial, and the ideals of personal empowerment and self-determination that are integral components of recovery-oriented mental health services.  Clinicians tend to assume that more treatment (i.e., higher attendance) is better than less treatment, whereas empowered consumers may prefer and benefit from less intervention.  A second limitation is that the study was conducted at only two sites in one region of the country and the sample was largely composed of middle-aged veterans who were longstanding recipients of VA mental health care.  Replication of these results with younger veterans and in a national sample would greatly enhance their generalizability.  Third, given the relatively short time frame of the study and the generally low attendance levels, it was not feasible to examine attendance patterns beyond the total number of groups attended during the study period.  It is possible that there are different predictors of attendance among those who attend a few groups and do not return versus those individuals who attend few groups at first, but more later on.  Finally, assessment of specific clinic and group leader characteristics might also be useful in understanding factors that affect attendance.

Future directions and clinical implications
The randomized study, of which this investigation is a component, is attempting to learn more about the content and outcome of recovery-oriented mental health groups.  Given the findings from this analysis, future work should focus in on mechanisms to increase motivation for treatment and also identification of appropriate treatment modalities. Though group leaders may often search for patient characteristics to explain non-attendance, one potentially obvious, but possibly overlooked, explanation may be that poor attendance is associated with the group not meeting patients’ needs (e.g., McKisack & Waller, 1996).  Alternatively, given that higher levels of education were predictive of increased attendance, it may be that a brief educational orientation to recovery-focused services may aid consumers in making an educated decision regarding the value of adding recovery-focused groups to their treatment plan. Pretherapy orientation has been shown to improve attendance at traditional psychotherapy groups (e.g., France & Dugo, 1985; Sheeran, Aubrey, & Kellett, 2007).  Finally, given that veterans have a choice between clinician-led and peer-led groups at their treatment facilities, future research might want to offer them an option to attend either and examine patterns of attendance when both  modalities are available. Beyond examination of attendance or adherence to recommended treatment interventions, recovery-focused, patient-centered care should continue to explore the kinds and quantities of services desired or preferred by consumers, as well as the links between services and clinical outcomes.
 


 

References:

Barber, J. A., Rosenheck, R. A., Armstrong, M., & Resnick, S. G. (2008). Monitoring the dissemination of peer support in the VA Healthcare System. Community Mental Health Journal, 44, 433–444.

Barbic, S., Krupa, T., & Armstrong, I. (2009). A randomized controlled trial of the effectiveness of a modified recovery workbook program: Preliminary findings. Psychiatric Services, 60(4), 491-497.

Brown, B. S., O’Grady, K. E., Farrell, E.V., Flechner, I.S., & Nurco, D.N. (2001). Factors Associated with Frequency of 12-Step Attendance by Drug Abuse Clients. American Journal of Drug and Alcohol Abuse, 27(1), 147–160

Burti, L., Amaddeo, F., Ambrosi, M., Bonetto, C., Cristofalo, D., Ruggeri, M., & Tansella, M. (2005). Does additional care provided by a consumer self-help group improve psychiatric outcome? A study in an Italian community-based psychiatric service. Community Mental Health Journal, 41(6), 705-720. DOI 10.1007/s10597-005-6428-1

Campbell, J. (2004). Consumer-operated services program (COSP) multisite research initiative overview and preliminary findings. Retrieved July 15, 2009 from http://www.power2u.org/downloads/COSPVAREPORT.pdf.

Centorrino, F., HernŠn, M. A., Drago-Ferrante, G., Rendall, M., Apicella, A., Lšngar, G., & Baldessarini, R. J. (2001). Factors associated with noncompliance with psychiatric outpatient visits. Psychiatric Services, 52(3), 378-380. doi:10.1176/appi.ps.52.3.378

Cocco, K. M., & Carey, K. (1998). Psychometric properties of the Drug Abuse Screening Test in outpatients. Psychological Assessment,19, 408-414.

Cook, J. A., Copeland, M.E., Hamilton, M.M., Jonikas, J.A., Razzano, L.A., Floyd, C.B., … & Grey, D.D. (2009). Initial outcomes of a mental illness self-management program based on Wellness Recovery Action Planning. Psychiatric Services, 60(2), 246–249.

Corrigan, P. W., Giffort, D., Rashid, F., Leary, M., & Okeke, I. (1999). Recovery as a psychological construct. Community Mental Health Journal, 35, 231-239. DOI:  10.1023/A:1018741302682

Davidson, L., Chinman, M. J., Kloos, B., Weingarten, R., Stayner, D., & Tebes, J. K. (1999). Peer support among individuals with severe mental illness: a review of the evidence. Clinical Psychology: Science and Practice, 6, 165–187. DOI:10.1093/schbul/sbj043

Davidson, L., Drake, R. E., Schmutte, T., Dinzeo, T., & Andres-Hyman, R. (2009). Oil and water or oil and vinegar? Evidence-based medicine meets recovery. Community Mental Health Journal, 45, 323–332. DOI 10.1007/s10597-009-9228-1

Davidson, L., & Roe, D. (2007). Recovery from versus recovery in serious mental illness: One strategy for lessening confusion plaguing recovery. Journal of Mental Health, 16(4), 1–12.

DiNitto, D. M., Webb, D. K., Rubin, A., Morrison-Orton, D., & Wambach, K. G. (2001). Self-help group meeting attendance among clients with dual diagnoses. Journal of Psychoactive Drugs, 33(3), 263-272.

Drake, R., & Bond, G. (2008). Supported employment: 1998 to 2008. Psychiatric Rehabilitation Journal, 31(4), 274-276. doi:10.2975/31.4.2008.274.276.

Eisen, S. V., Normand, S. L. T., Belanger, A., Spiro, A., III, & Esch, D. (2004). The revised Behavior and Symptom Identification Scale (BASIS-24): Reliability and Validity. Medical Care, 42, 1230-1241.

Falzer, P. R. (2011). Comparing an intervention with treatment as usual. Psychiatric Services, 62(7), 807.

Fenger, M., Mortensen, E., Poulsen, S., & Lau, M. (2011). No-shows, drop-outs and completers in psychotherapeutic treatment: Demographic and clinical predictors in a large sample of non-psychotic patients. Nordic Journal of Psychiatry, 65(3), 183-191.

First, M. B., Spitzer, R. L., Gibbon, M., & Williams, J. B. W. (1997). Structured Clinical Interview for DSM-IV Axis I Disorders (Patient ed., Version 2). New York: Biometrics
Research Department, New York State Psychiatric Institute.

France, D. G., & Dugo, J. M. (1985). Pretherapy orientation as preparation for open psychotherapy groups. Psychotherapy, 22, 256-261.

Fukui, S., Davidson, L., Holter, M., & Rapp, C. (2010). Pathways to Recovery (PTR): Impact of peer-led group participation on mental health recovery outcomes. Psychiatric Rehabilitation Journal, 34(1), 42-48. doi:10.2975/34.1.2010.42.48.

Goldberg, R., & Resnick, S. (2010). US Department of Veterans Affairs (VA) efforts to promote psychosocial rehabilitation and recovery. Psychiatric Rehabilitation Journal, 33(4), 255-258. doi:10.2975/33.4.2010.255.258.

Hebert, M., Rosenheck, R., Drebing, C., Young, A. S., & Armstrong, M. (2008).  Integrating peer support initiatives in a large healthcare organization. Psychological Services, 5(3), 216-227. DOI: 10.1037/1541-1559.5.3.216

Hibbard, J. H., Stockard, J., Mahoney, E. R., & Tusler, M. (2004). Development of the Patient Activation Measure (PAM): Conceptualizing and measuring activation in patients and consumers. Health Services Research, 39, 1005-1024.

Hilbe, J. M. (2007). Negative Binomial Regression. Cambridge, UK: Cambridge University.

Ilardi, D. L., & Kaslow, N. J. (2009). Social difficulties influence group psychotherapy adherence in abused, suicidal African American women. Journal of Clinical Psychology, 65(12), 1300-1311. doi:10.1002/jclp.20628

Jonikas, J.A., Kiosk, S., Grey, D. D., Hamilton, M. M., McNulty, J., & Cook, J. A. (2010). Cultural competency in peer-run programs: Results of a web survey and implications for
future practice. Psychiatric Rehabilitation Journal, 34(2), 121–129.

Kazis, L.E., Miller, D.R., Clark, J.A. et al. (2004). Improving the response choices on the veterans SF-36 health survey role functioning scales. Results from the Veterans Health Study. Journal of Ambulatory Care Management, 27, 263-280.

Kelly, J. F., & Moos, R. (2003). Dropout from 12-step self-help groups: Prevalence, predictors, and counteracting treatment influences. Journal of Substance Abuse Treatment, 24, 241–250.

Laudet, A.B., Magura, S., Cleland, C.M., Vogel, H.S., & Knight, E. L. (2003). Predictors of retention in dual-focus self-help groups. Community Mental Health Journal, 39(4), 281-295.
Luke, D. A., Roberts, L., & Rappaport, J. (1993). Individual, group context, and individual-group fit predictors of self-help group attendance. Journal of Applied Behavior Science, 29, 216-238.

McKisack, C., & Waller, G. (1996). Why is attendance variable at groups for women with Bulimia Nervosa? The role of eating psychopathology and other characteristics. International Journal of Eating Disorders, 20, 205-209.

O'Connell, M., Kasprow, W., & Rosenheck, R. (2010). National dissemination of supported housing in the VA: Model adherence versus model modification. Psychiatric Rehabilitation Journal, 33(4), 308-319. doi:10.2975/33.4.2010.308.319.

Ogrodniczuk, J. S., Piper, W. E., & Joyce, A. S. (2006). Treatment compliance in different types of group psychotherapy: Exploring the effect of age. Journal of Nervous and Mental Disorders, 194, 287–293. DOI: 10.1097/01.nmd.0000207366.49820.85

Paulson, R., Herinckx, H., Demmler, J., Clarke, G., Cutter, D., & Birecree, E. (1999). Comparing practice patterns of consumer and non-consumer mental health service. Community Mental Health Journal, 35, 251-269. DOI: 10.1023/A:1018745403590

Rapp, C., & Goscha, R. (2004). The principles of effective case management of mental health services. Psychiatric Rehabilitation Journal, 27(4), 319-333.

Resnick, S. G., Armstrong, M., Sperrazza, M., Harkness, L., & Rosenheck, R. A. (2004). A model of consumer-provider partnership: Vet-to-Vet. Psychiatric Rehabilitation Journal, 28, 185-187.

Resnick, S. G., & Rosenheck, R. A. (2008). Integrating peer support and mental health services: A quasi-experimental study of recovery orientation, confidence and empowerment. Psychiatric Services, 59, 1307-1314.

Resnick, S. G., & Rosenheck, R. A. (2010). Who attends Vet-to-Vet? Predictors of attendance in mental health mutual support. Psychiatric Rehabilitation Journal, 33, 262-268.

Ridgway, P., McDiarmid, D., Davidson, L., Bayes, J., & Ratzlaff, S. (2002). Pathways to Recovery: A Strengths Recovery Self-Help Workbook. Lawrence, KS: The University of Kansas School of Social Welfare.

Rogers, E. S., Chamberlin, J., Ellison, M. L., & Crean, T. (1997). A consumer-constructed scale to measure empowerment among users of mental health services. Psychiatric Services, 48, 1042-1047.

SAS Institute Inc. (2008). SAS software, Version 9.2. Cary, NC, USA.

Saunders, J.B., Aasland, O. F., Babor, T. F., De La Fuente, J.R., & Grant, M. (2006). Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction, 88, 791-804.

Schneider, R., Burnette, M., & Timko, C. (2008). History of physical or sexual abuse and participation in 12-step self-help groups. The American Journal of Drug and Alcohol Abuse, 34: 617–625.

Sells, D., Davidson, L., Jewell, C., Falzer, P., & Rowe, M. (2006). The treatment relationship in peer-based and regular case management for clients with severe mental illness. Psychiatric Services, 57, 1179–1184.

Sheeran, P., Aubrey, R., & Kellett, S. (2007). Increasing attendance for psychotherapy: Implementation intentions and the self-regulation of attendance-related negative affect. Journal of Consulting and Clinical Psychology, 75(6), 853-863. doi:10.1037/0022-006X.75.6.853

Sherbourne, C. D., & Stewart, A. L. (1991). The MOS Social Support Survey. Social Science & Medicine, 32, 705-714.

Sledge, W. H., Lawless, M., Sells, D., Wieland, M., O'Connell, M. J., & Davidson, L. (2011). Effectiveness of peer support in reducing readmissions of persons with multiple psychiatric hospitalizations. Psychiatric Services, 62(5), 541-544.

Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., et al. (1991). The will and the ways: development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60, 570-585.

Solomon, P., & Draine, J. (2001). The state of knowledge of the effectiveness of consumer provided services. Psychiatric Rehabilitation Journal, 25, 20–27.

Spaniol, L., Koehler, M., & Hutchinson, D. (1994, 2009). The recovery workbook: Practical coping and empowerment strategies for people with psychiatric disability, Revised edition. Boston: Boston University, Center for Psychiatric Rehabilitation.

SPSS Inc . (2009). SPSS for Windows, Rel. 18.0. Chicago, IL.

Starnino, V., Mariscal, S., Hotler, M., Davidson, L., Cook, K., Fukui, S., & Rapp, C. (2010. Outcomes of an illness self-management group using wellness recovery action planning.
Psychiatric Rehabilitation Journal, 34(1), 57-60.

Swarbrick, M., Schmidt, L. T., & Pratt, C. W. (2009). Consumer-operated self-help centers: Environment, empowerment, and satisfaction. Journal of Psychosocial Nursing, 47(7), 41-47.

Uniformed Mental Health Services Handbook (VHA HANDBOOK 1160.010. June 11, 2008).

Weiss, R. D., Griffin, M. L., Gallop, R., Luborsky, L., Siqueland, L., Frank. A., Onken, L. S., Daley, D. C., & Gastfriend, D. R. (2000). Predictors of self-help group attendance in cocaine dependent patients. Journal of Studies on Alcohol, 61(5), 714-719.

Weiss, R. D., Griffin, M. L., Gallop, R. J., Najavits, L. M., Frank, A., Crits-Christoph, P., Thase, M. E., Blaine, J., Gastfriend, D. R., Daley, D., & Luborsky, L. (2005). The effect of 12-step self-help group attendance and participation on drug use outcomes among cocaine-dependent patients. Drug and Alcohol Dependence, 77, 177–184.

Yanos, P. T., Primavera, L. H., & Knight, E. L. (2001). Consumer-run participation, recovery in social functioning, and the mediating role of psychological factors. Psychiatric Services, 52(4), 493-500.

Zemore, S.E., & Kaskutas,  L.A. (2008). Services received and treatment outcomes in day-hospital and residential programs. Journal of Substance Abuse Treatment, 35, 232–244.

 


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