The Health Behavior Model of Personality in the Context of a Public Health Crisis
Bibliographic record
Abstract
OBJECTIVE: The US Centers for Disease Control and Prevention recommended behavioral measures to slow the spread of COVID-19, such as social distancing and wearing masks. Although many individuals comply with these recommendations, compliance has been far from universal. Identifying predictors of compliance is crucial for improving health behavior messaging and thereby reducing disease spread and fatalities. METHODS: We report preregistered analyses from a longitudinal study that investigated personality predictors of compliance with behavioral recommendations in diverse US adults across five waves from March to August 2020 (n = 596) and cross-sectionally in August 2020 (n = 405). RESULTS: Agreeableness-characterized by compassion-was the most consistent predictor of compliance, above and beyond other traits, and sociodemographic predictors (sample A, β = 0.25; sample B, β = 0.12). The effect of agreeableness was robust across two diverse samples and sensitivity analyses. In addition, openness, conscientiousness, and extraversion were also associated with greater compliance, but effects were less consistent across sensitivity analyses and were smaller in sample A. CONCLUSIONS: Individuals who are less agreeable are at higher risk for noncompliance with behavioral mandates, suggesting that health messaging can be meaningfully improved with approaches that address these individuals in particular. These findings highlight the strong theoretical and practical utility of testing long-standing psychological theories during real-world crises.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".