Differences in intent to refer buprenorphine among community correctional and treatment staff: A set of cross-lagged models predicting efficacy beliefs and familiarity with buprenorphine for opioid use disorder

Background and Objectives: Despite high rates of individuals with opioid use disorder, community correctional agencies underutilize medications for opioid use disorder (MOUD). Knowledge about the mechanisms which motivate correctional employees to refer buprenorphine remains underdeveloped, and differences in these patterns by employee status are unknown. This study has two objectives: (1) investigate the presence of a reciprocal relationship between familiarity with buprenorphine and efficacy beliefs among community corrections and community treatment staff and (2) identify whether this relationship differs by staff status in referral intentions. Methods: Data were used from the Criminal Justice Drug Abuse Treatment Studies 2 (CJ-DATS 2) among correctional and treatment employees (N = 873). Four models investigated whether a reciprocal relationship existed between buprenorphine familiarity and efficacy beliefs. Then, the best fitting model was used to test the influence that prior training had on future referral intention through familiarity and efficacy beliefs among the analytic sample (n = 612), by comparing two separate structural equation models (SEMs) among correctional staff and treatment staff, respectively. Results: The fully cross-lagged model provided a significantly better fit to the data than other models ((Figure presented.) (1) = 7.189, p <.01). The results of the multigroup SEM show that training had positive, indirect effects on future referral intentions that significantly differed between treatment and community correction staff. Discussion and Conclusions: Findings show that training may influence correctional staff intent to refer individuals to receive buprenorphine through familiarity. Scientific Significance: Tailored training for MOUD treatment for specific staff populations may prove more beneficial than existing approaches.