Data analysis
REGRESSION, REGRESSION
This analysis was conducted using Stata version 14.2 (Stata Corp, College Station, TX, 176 USA). Stakeholder and facility-level characteristics were summarised using frequencies, proportions, means and standard deviations (SD) or medians and their interquartile ranges (IQR), stratified by intervention group (i.e., CombinADO package and ESOC package). Bivariate analyses for comparison of predictor variables and attitude scores across intervention groups were performed using a t-test or Wilcoxon rank-sum test for continuous data and Chi-square or Fisher's exact test as appropriate for categorical data.Regression analyses were considered to understand the association of total attitude scores with stakeholder and facility-level characteristics. Given that the data from this cross-sectional study were embedded within a cRCT, we first had to consider the effect of clustering of the stakeholders―stakeholders being nested within clinics―on reported attitude scores to ensure results were not biased. We estimated a null model (i.e., containing no explanatory variables) to test for random effects of within and between clinic variations by computing an intraclass correlation (ICC). The model revealed that respondents' attitude scores were not influenced by site clustering, thus showing that multilevel modelling may not be warranted. We used simple linear regression models to identify explanatory variables that significantly predicted stakeholders' attitude scores for adopting the intervention packages. Finally, we fitted a multivariable linear regression model that included explanatory variables found to be significant at 