Statistical Analysis
REGRESSION, SYNDROME, SYNDROME, REGRESSIONS
We assumed no change in preintervention scores and postintervention scores for the control group and used estimates for the SD of the change in MBI scores from our pilot data.Descriptive statistics were computed for characteristics of the whole cohort and by intervention, with comparisons made using Wilcoxon rank sum tests for continuous covariates and Fisher exact or χAn intent-to-treat analysis was performed on all participants regardless of postsurvey completion using linear and logistic mixed-effects models including the main effects of the time period (baseline vs postintervention), treatment (intervention vs control), the interaction between time period and treatment, and a random intercept for participants estimated using restricted maximum likelihood. Mean change from baseline within each group and the difference in mean change between groups and their 95% CIs were reported. Mixed-effects logistic regression models were used for EE, PA, DP, Young Impostor Syndrome Scale, Moral Injury Symptom Scale-Healthcare Professionals, Self-Compassion Scale-Short Form, Secure Flourish Index and for the binary outcomes of burnout and impostor syndrome.Linear and logistic regression models were used to estimate mean change from baseline and odds ratios (ORs) adjusted for baseline value and their 95% CIs. The binary outcomes of burnout and impostor syndrome at follow-up were separately modeled using logistic regressions with treatment group and baseline values as variables. Effect sizes (risk difference, number needed to treat [NNT], and relative risk) for burnout and impostor syndrome were estimated from models using the same counterfactual framework for marginal effects. The 95% CIs were estimated with simulation.In sensitivity analyses, to assess the potential influence of missing follow-up survey data on study outcomes, multiple imputation was used for missing score values via iterative chained random forestAll 