Data analyses
REGRESSION
We report demographic and tobacco use characteristics for staff (Randomized trial guidelines are that participant characteristics in each condition should not be compared at baseline [As all 7 programs received the intervention at some point, we also collapsed across condition and compared outcomes pre-post intervention. There were 8 selected outcomes (4 staff outcomes and 4 client outcomes). For each outcome, we used a linear regression model including time (pre-post intervention) as a predictor, adjusted for demographic characteristics (age, gender, race/ethnicity, education) and for nesting of participants within clinic. Generalized estimating equations with logit link were used for dichotomous outcomes, and mixed-effects regression models were used for continuous outcomes. Client analyses assume independent samples because California Medi-Cal pays for 90 days of treatment, so the same client would not have been in treatment at both timepoints. However, the models allowed for correlations within staff who completed surveys at both data collection points. Last, in a sensitivity analysis, the regression models were repeated but removing data for one control program (here identified as Program 5) that implemented tobacco-free policies shortly after baseline.