Missing participants and missing data handling
Data was collected by gathering participating students in an auditorium and providing them with time to complete the assessment. Therefore, missing participants fall into two categories: drop-out participants and absent participants. Drop-out participants occurred when they informed the intervention implementer of their withdrawal from the study. Except for one class (n = 48) students who withdrew from the study due to one subject teacher believed that the students should spend more time studying, after two weeks from the start of the intervention, there were no more students or classes who withdrew. In this case, they did not complete the post-intervention assessment (T1) or follow-up assessment (T2). Absent participants also occurred when they were not present at school on the day of assessment. Therefore, participants who were missing from the post-intervention assessment (T1) might be present at T2 and completed the follow-up assessment (T2). Therefore, the number of lost participants at T2 were counted in comparison to the baseline participants.Missing data occurred in two different scenarios. The first scenario was when participants dropped out of the intervention or were not present on the day of assessment. At time point 1, there were 133 missing participants. Of those, 116 were from the intervention group, with 48 dropping out of the intervention and 68 not being present on the assessment day, while 17 were from the control group. At time point 2, there were 99 missing participants, with 17 from the control group and 82 from the intervention group. For the missing participants from the intervention group at time point 2, 48 had already dropped out at time point 1, and 34 were not present on the day of assessment at time point 2. Even though some participants did not complete all the time points, their data was still used in the analyses. The participants who dropped out did not differ significantly from those who remained in the trial regarding participant characteristics and study outcomes.The second scenario was when a single participant skipped questions on a questionnaire such as PSS-10 or DASS-21. In this case, the missing data points were coded as NA and treated as missing in our analysis in R. Any participants with missing data for given outcomes were removed from the analysis of those outcomes. Missing data were not significantly associated with participants’ characteristics or study outcomes. To test the robustness of our results, we compared LMM results of including only those with complete data with results of including all the participants and found that the two results remained almost unchanged.