Characterizing the heterogeneity of clinician practice use in community mental health using latent profile analysis

Abstract

Abstract Background The behavioral health service provider population is highly heterogeneous. However, it is rarely treated as such within evidence-based practice implementation efforts. This study aimed to evaluate, as a proof of concept, the utility of latent profile analysis to identify distinct profiles of clinician practices in a large sample of youth-serving community mental health clinicians. This study also aimed to identify predictors of profile membership to inform implementation efforts. Methods Participants were 484 practicing clinicians (79.4% female, 45.7% White, M age = 37.1 years). As part of a larger survey, clinicians reported on their use of cognitive, behavioral, family, and psychodynamic treatment techniques with a representative client on their caseload. Latent profile analysis was used to determine the presence of clinician practice profiles. Multilevel multinomial logistic regressions examined predictors of profile membership. Results Latent profile analysis indicated a 4-profile solution best fit the data, with clinicians who: 1) used generally low levels of all examined techniques and preferred cognitive techniques (Low Eclectics, 16%), 2) delivered moderate levels of all techniques (Moderate Eclectics, 53%), 3) demonstrated preference for use of family techniques (Family Preferred, 11%), and 4) used high levels of all techniques (Super Users, 20%). Clinician discipline (e.g., social work), education, and years of experience predicted profile membership. Conclusions Findings from this proof of concept study underscore the utility of latent profile analysis to characterize the complex and heterogeneous makeup of community mental health. Results extend prior work highlighting the eclectic nature of community mental health practice. Predictor analyses underscore the important influence of clinician background characteristics on practice use.

Keywords

• Usual care
• Implementation science
• Youth mental health
• Latent profile analysis