Data treatment and analysis
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REGRESSION
Achieving the originally envisioned sample size proved challenging. As outlined above, we sampled participants from more barangays than initially envisioned, (a total of 8 in control and 4 intervention barangays across urban and rural study sites), following the pre-defined procedures, due to difficulties reaching participants fulfilling the inclusion criteria under pandemic conditions, and as many potential participants refused to participate online. We considered shifting to in-person data collection but ultimately decided against it due to the COVID-19 concerns raised by the selected barangays and budgetary constraints. Instead, we analysed the data sets available after more than 1 year of data collection (n=719) to see if adding more information could impact the primary outcomes. We calculated whether knowledge had increased in total (ie, the total points in the suggested approach are higher in pre-intervention and post-intervention) and in which particular domain knowledge had increased. Furthermore, we did linear regression analysis with ‘change in scores’ as the result, ‘intervention’ as the major factor and ‘demographics’ as the other components. As there was little room for improvement via the intervention because of the high percentage of participants in the intervention and control groups expressing the desired response to D1 (‘delays in taking vaccines’), we concluded that additional sampling to include the originally envisioned n=800 participants could not change the study’s conclusions with regard to this outcome. Secondary outcomes would similarly be unaffected by additional data collection. We therefore stopped collecting data and analysed the available data sets.Overall, we assessed the binary improvement (improvement vs no improvement) and the amount of improvement in vaccine attitudes and intentions after intervention exposure. The PACV-15 Likert Scales were labelled D4–D13 (10 items), and the responses were transformed from a 1 to 5 scale to a −2 to 2 scale, with −2 representing the least desired option (regardless of whether that was 1 or 5 in the original scale). The more items we included, the more granular the differences we could discern both person-to-person and within a person over time. Additionally, the Likert scale responses were analysed inferentially by coding answers from 1 (strongly disagree) to 5 (strongly agree) and drawing on parametrical (paired t-test) or non-parametrical (Wilcoxon signed rank test) approaches, depending on sample characteristics. Electronic copies of all data were saved offline and external hard drives were stored in a locked cabinet at the Research Institute for Tropical Medicine in the Philippines. All data management and analyses were performed using STATA (Stata Corp, College Station, Texas, USA) and R (R Development Core Team, Vienna, Austria) statistical software. An author reflexivity statement on our partnership is included as 
