NIDA Roundtable Meeting on Evidence-Based Treatments for Substance Abuse in Indian Country

1. Frank Big Bear Research OverviewTraci Rieckmann, Ph.D. Oregon Health and Sciences UniversityDoug NOvinS, M.D., University of Colorado DenverLaurie MoOre, M.p.H. University of Colorado Denver NIDA Roundtable Meeting on Evidence-Based Treatments for Substance Abuse in Indian Country

2. Role of the Provider • Counselors/providers play significant role • quality of care • patient satisfaction and outcomes • implementation of new practices. • Provider support varies depending on intervention, counselor education level, and experience. • Adoption of new therapies requires marketing of interventions, supervision, exposure to the innovative, practice, and monitoring for adherence (Abraham, et al., 2009; Santisteban, Vega, & Suarez-Morales, 2006; Knudsen, et al., 2007).

3. The Current Workforce • Counseling workforce is estimated to include approximately 200,000 individuals (SAMHSA, 2013; Libretto et al, 2004). • Workforce analysis suggests majority of treatment professionals are Caucasian, middle-aged, and more often female than male (SAMHSA, 2013; Abraham, Ducharme, & Roman, 2009; Mulvey et al., 2003; Libretto et al., 2004).  • Counseling workforce is projected to increase 20-24% by 2018 (SAMHSA, 2013).

4. Workforce Studies • Characteristics individual • Training • Credentials/competencies • Attitudes and experiences • Toward EBPs • Administration and leadership • Intentions to use innovation • Results often vary across: • Treatment Setting (funding sources: Public, private) • Time frame

5. Workforce Studies

6. Transitions and Growth • Annual turnover rate is cited as being anywhere between 18% and 50% (Rothrauff et al., 2011; Eby, et al., 2010; Johnson et al., 2002; McNulty et al., 2007; Mclellan et al., 2003). • Projected growth across disciplines: • Substance Abuse & Behavioral Disorders Counselors 21% • Mental Health Counselors 24% • Mental Health & Substance Abuse Social Workers 20% • Psychologists 11% To date, no national study has focused on the characteristics of counselors working in AI/AN treatment programs.

7. DATA ANALYSIS METHODS • Sample (n=192) • Analytic methods used • Descriptive analyses by program location (rural/urban) were completed on all variables that described either respondent, staff, or program characteristics; • Variables with p-values <=0.25 in a univariate regression with program location (rural/urban) were entered into logistic regression models that follow

8. TABLE 1a: Characteristics of Staff of 192 Substance Abuse Programs by Program Location

9. TABLE 1b: Characteristics of Staff of 192 Substance Abuse Programs Participating in the Evidence-Based Practices Survey, by Program Location

10. TABLE 1c: Characteristics of Staff of 192 Substance Abuse Programs Participating in the Evidence-Based Practices Survey, by Program Location

11. TABLE 2a: Program Characteristics of 192 Substance Abuse Programs Participating in the Evidence-Based Practices Survey, by Program Location

12. TABLE 2b: Program Characteristics of 192 Substance Abuse Programs Participating in the Evidence-Based Practices Survey, by Program Location

13. Table 3a. Logistic regression model predicting rural program location with staff characteristics as predictors.

14. Staffing Characteristics Programs in rural locations are LESS likely to • Have nurses on staff • Have traditional healing consultants on staff

15. Table 3b. Logistic regression model predicting rural program location with program characteristics as predictors.

16. Program Characteristics Rural programs are MORE likely to Rural programs are LESS likely to • Struggle to recruit and retain staff • Offer AA-open group recovery services • Offer equine therapy • Offer religious and meditation services • Offer medication therapies • Offer pipe ceremonies and cultural activities

17. Table 3c. Logistic regression model predicting rural program location with program assessment characteristics as predictors.

18. Program Assessment Rural programs are MORE likely to Rural programs are LESS likely to • Collect data on treatment outcomes • Use an outside evaluator • Participate in research or program evaluation