Factors Associated with Nonspecialist Quality of Delivery within a Family Strengthening Intervention in Rwanda: a Parallel Latent Growth Model.

In task-shared, mental health, and psychosocial support interventions, monitoring the quality of delivery (fidelity and competence) of nonspecialist providers is critical. Quality of delivery is frequently reported in brief, summary statistics, and while both fidelity and competence scores tend to be high, rarely have factors associated with quality of delivery in low-resource, mental health, and psychosocial support interventions been examined using inferential statistics. Understanding both modifiable and non-modifiable predictors of quality of delivery is important for adapting training and supervision approaches throughout intervention delivery. In this study, we use a parallel process latent growth model to examine the association of non-modifiable, demographic characteristics of nonspecialists and changes in both fidelity and competence over time. We find that nonspecialist age is significantly associated with higher initial fidelity and competence scores and smaller improvements in fidelity and competence over time, although this finding is interpreted in the presence of ceiling effects. In addition, nonspecialists in a certain district were more likely to have higher initial fidelity and competence scores but also see smaller changes over time. Fidelity and competence were found to significantly co-vary. This study provides conceptual and measurement guidance regarding quality of delivery, suggesting that fidelity and competence are theoretically distinct and must be measured separately, but linked together under the umbrella of quality of delivery. This study also has implications for recruiting, training, and supporting nonspecialists delivering behavioral interventions, suggesting that future implementation teams can further contribute to research on how to better support high-quality training, supervision, and personal and professional growth among the growing nonspecialist workforce globally.