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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".