Persuasive Messages for Improving Adherence to COVID-19 Prevention Behaviors: Randomized Online Experiment
Bibliographic record
Abstract
BACKGROUND: Adherence to nonpharmaceutical interventions for COVID-19, including physical distancing, masking, staying home while sick, and avoiding crowded indoor spaces, remains critical for limiting the spread of COVID-19. OBJECTIVE: The aim of this study was to test the effectiveness of using various persuasive appeals (deontological moral frame, empathy, identifiable victim, goal proximity, and reciprocity) at improving intentions to adhere to prevention behaviors. METHODS: A randomized online experiment using a representative sample of adult Canadian residents with respect to age, ethnicity, and province of residence was performed from March 3 to March 6, 2021. Participants indicated their intentions to follow public health guidelines, saw one of six flyers featuring a persuasive appeal or no appeal, and then rated their intentions a second time. Known correlates of attitudes toward public health measures were also measured. RESULTS: Intentions to adhere to public health measures increased in all appeal conditions. The message featuring an empathy appeal resulted in a greater increase in intentions than the control (no appeal) message. Moreover, the effectiveness of persuasive appeals was moderated by baseline intentions. Deontological, empathy, identifiable victim, and reciprocity appeals improved intentions more than the control message, but only for people with lower baseline intentions to adhere to nonpharmaceutical interventions. CONCLUSIONS: Public health marketing campaigns aiming to increase adherence to COVID-19 protective behaviors could achieve modest gains by employing a range of persuasive appeals. However, to maximize impact, it is important that these campaigns be targeted to the right individuals. TRIAL REGISTRATION: ClinicalTrials.gov NCT05722106; https://clinicaltrials.gov/ct2/show/NCT05722106.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".