2.5. Analyses
REGRESSION, REGRESSIONS
All statistical analyses were completed using the Statistical Package for the Social Sciences (SPSS) version 26 and R version 4.1.0. Descriptive statistics were used to describe the sample in terms of background characteristics. Group differences in baseline demographic and clinical variables were assessed using independent samples t-tests for continuous (e.g., age) variables and Pearson chi-square tests for categorical (e.g., ethnicity, province of residence) variables. Next, the number of participants requesting and receiving n accommodation was examined, but no group comparisons were made given the low frequency of both requesting and receiving accommodations.Generalized estimating equation models (GEEs) [A modified intention-to-treat (ITT) analysis was used to account for participant noncompliance and missing data [Logistic regression models were conducted to examine whether baseline ratings of supervisor leadership (SSLS) and organizational inclusiveness (CIS) were associated with requesting or receiving mental health accommodations. A linear regression model was used to examine whether ratings of supervisor leadership and organizational inclusiveness predicted comfort with and rates of disclosing mental health condition(s). Specifically, logistic regressions were used in models for requesting and receiving accommodations given these outcome variables are dichotomous, and linear regressions were used for comfort disclosing and rates of disclosure given these outcome variables are continuous.