Mimi Choy-Brown’s research while affiliated with University of Minnesota, Duluth and other places

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Publications (1)

Hypothesized 3-factor model (A) and competing 1-factor model (B) of EBCSS and BSWAI items. Note: N = 181 clinicians. Models estimated using robust maximum likelihood estimation; standardized estimates shown. EBCSS, evidence-based clinical supervision strategies scale; BSWAI-T, brief supervisory working alliance inventory—trainee form; active, active learning subscale of the EBCSS; audit, audit and feedback subscale of the EBCSS; alliance, second-order supervision working alliance factor of the BSWAI-T; focus, client focus subscale of the BSWAI-T; rapport, rapport subscale of the BSWAI-T. Model A: χ² = 36.12, df = 30, p = 0.204; RMSEA = 0.034; CFI = 0.990; SRMR = 0.031. Model B: χ² = 55.13, df = 31, p = 0.005; RMSEA = 0.066; CFI = 0.962; SRMR = 0.067. Results of a Satorra-Bentler scaled chi-square difference test indicated Model A fit significantly better than Model B (S-B Scaled χ² Δ = 39.40, df = 1, p = 0.000)
Aim 2 confirmatory factor analysis model
Adjusted mean differences in clinicians’ experience of EBCSS clinical supervision techniques by level of agency climate for EBP implementation. Note: K = 21 mental health clinics, N = 147 clinicians. Adjusted means are estimated using linear 2-level mixed effects regression models with random intercepts; all models control for clinician years of experience and education. EBCSS, Evidence-based Clinical Supervision Strategies scale. ICC[1] for Audit and Feedback = 0.095; ICC[1] for Active Learning = 0.241
Psychometric evaluation of a pragmatic measure of clinical supervision as an implementation strategy
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April 2023

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Implementation Science Communications

Mimi Choy-Brown

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Susan Esp

Background Valid and reliable measurement of implementation strategies is essential to advancing implementation science; however, this area lags behind the measurement of implementation outcomes and determinants. Clinical supervision is a promising and highly feasible implementation strategy in behavioral healthcare for which pragmatic measures are lacking. This research aimed to develop and psychometrically evaluate a pragmatic measure of clinical supervision conceptualized in terms of two broadly applicable, discrete clinical supervision techniques shown to improve providers’ implementation of evidence-based psychosocial interventions—(1) audit and feedback and (2) active learning. Methods Items were generated based on a systematic review of the literature and administered to a sample of 154 outpatient mental health clinicians serving youth and 181 community-based mental health providers serving adults. Scores were evaluated for evidence of reliability, structural validity, construct-related validity, and measurement invariance across the two samples. Results In sample 1, confirmatory factor analysis (CFA) supported the hypothesized two-factor structure of scores on the Evidence-Based Clinical Supervision Strategies (EBCSS) scale (χ²=5.89, df=4, p=0.208; RMSEA=0.055, CFI=0.988, SRMR=0.033). In sample 2, CFA replicated the EBCSS factor structure and provided discriminant validity evidence relative to an established supervisory alliance measure (χ²=36.12, df=30, p=0.204; RMSEA=0.034; CFI=0.990; SRMR=0.031). Construct-related validity evidence was provided by theoretically concordant associations between EBCSS subscale scores and agency climate for evidence-based practice implementation in sample 1 (d= .47 and .55) as well as measures of the supervision process in sample 2. Multiple group CFA supported the configural, metric, and partial scalar invariance of scores on the EBCSS across the two samples. Conclusions Scores on the EBCSS provide a valid basis for inferences regarding the extent to which behavioral health providers experience audit and feedback and active learning as part of their clinical supervision in both clinic- and community-based behavioral health settings. Trial registration ClinicalTrials.gov NCT04096274. Registered on 19 September 2019.

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