Abstract
Objective: Children infrequently receive evidence-based treatments (EBTs) for mental health problems due to a science-to-practice implementation gap. Workplace-based clinical supervision, in which supervisors provide oversight, feedback, and training on clinical practice, may be a method to support EBT implementation. Our prior research suggests that the intensity of supervisory focus on EBT (i.e., thoroughness of coverage) during workplace-based supervision varies. This study explores predictors of supervisory EBT intensity.Methods: Participants were twenty-eight supervisors and 70 clinician supervisees. They completed a baseline survey, and audio recorded supervision sessions over 1 year. Four hundred and thirty eight recordings were coded for supervision content. We chose to explore predictors of two EBT content elements due to their strong evidence for effectiveness and sufficient variance to permit testing. These included a treatment technique (“exposure”) and a method to structure treatment (“assessment”). We also explored predictors of non-EBT content (“other topics”). Mixed-effects models explored predictors at organizational/supervisor, clinician, and session levels.Results: Positive implementation climate predicted greater intensity of EBT content coverage for assessment (coefficient = 0.82, p = 0.004) and exposure (coefficient = 0.87, p = 0.001). Intensity of exposure coverage was also predicted by more time spent discussing each case (coefficient = 0.04, p < 0.001). Predictors of greater non-EBT content coverage included longer duration of supervision sessions (coefficient = 0.05, p < 0.001) and lower levels of supervisor EBT knowledge (coefficient = −0.17, p = 0.013). No other supervisor- or clinician-level variables were significant predictors in the mixed effects models.Conclusion: This was the first study to explore multi-level predictors of objectively coded workplace-based supervision content. Results suggest that organizations that expect, support and reward EBT are more likely to have greater intensity of EBT supervision coverage, which in turn may positively impact clinician EBT fidelity and client outcomes. There was evidence that supervisor knowledge of the EBT contributes to greater coverage, although robust supervisor and clinician factors that drive supervision are yet to be identified. Findings highlight the potential effectiveness of implementation strategies that simultaneously address organizational implementation climate and supervisor practices. More research is needed to identify mechanisms that support integration of EBT into supervision.
Highlights
Many evidence-based treatments (EBTs) have been developed to address child and adolescent mental health needs [1]
We explored for the impact of other supervisor characteristics that not specific to EBT, may play a role in supervision content coverage, including years of experience conducting therapy, percent of time providing supervision, and their own ongoing involvement in providing therapy
A null model predicting intensity of exposure coverage indicated that 16% of the variance in exposure coverage was at the supervisor level and 19% at the clinician level, with the remaining 65% at the individual supervision-session level
Summary
The potential promise of EBTs has not been realized due to the substantial challenge of implementing them in community mental health settings [2,3,4]. Growing consensus in the literature indicates that EBTs are implemented at a slow pace in community settings, leading to critical gaps in the quality and effectiveness of mental health care [5,6,7]. In the Exploration, Adoption/Preparation, Implementation, and Sustainment model of EBT implementation (EPIS) [10], fidelity monitoring and support—important aspects of EBT-focused clinical supervision—are noted as inner setting factors affecting implementation. Without ongoing clinical supervision focused on the EBT, clinicians’ fidelity can be low [11, 12], creating challenges for both the active implementation and sustainment phases of EBT implementation [10]
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