Assessment of the Effectiveness of Identity-Based Public Health Announcements in Increasing the Likelihood of Complying With COVID-19 Guidelines: Randomized Controlled Cross-sectional Web-Based Study
Bibliographic record
Abstract
BACKGROUND: Public health campaigns aimed at curbing the spread of COVID-19 are important in reducing disease transmission, but traditional information-based campaigns have received unexpectedly extreme backlash. OBJECTIVE: This study aimed to investigate whether customizing of public service announcements (PSAs) providing health guidelines to match individuals' identities increases their compliance. METHODS: We conducted a within- and between-subjects, randomized controlled cross-sectional, web-based study in July 2020. Participants viewed two PSAs: one advocating wearing a mask in public settings and one advocating staying at home. The control PSA only provided information, and the treatment PSAs were designed to appeal to the identities held by individuals; that is, either a Christian identity or an economically motivated identity. Participants were asked about their identity and then provided a control PSA and treatment PSA matching their identity, in random order. The PSAs were of approximately 100 words. RESULTS: We recruited 300 social media users from Amazon Mechanical Turk in accordance with usual protocols to ensure data quality. In total, 8 failed the data quality checks, and the remaining 292 were included in the analysis. In the identity-based PSA, the source of the PSA was changed, and a phrase of approximately 12 words relevant to the individual's identity was inserted. A PSA tailored for Christians, when matched with a Christian identity, increased the likelihood of compliance by 12 percentage points. A PSA that focused on economic values, when shown to individuals who identified as economically motivated, increased the likelihood of compliance by 6 points. CONCLUSIONS: Using social media to deliver COVID-19 public health announcements customized to individuals' identities is a promising measure to increase compliance with public health guidelines. TRIAL REGISTRATION: ISRCTN Registry 22331899; https://www.isrctn.com/ISRCTN22331899.
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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 | 0.064 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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; both teacher heads agree on what is shown here.
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".