Emotions, reasoning, and mental health as predictors of behavior during three phases of the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has required people worldwide to adjust their behavior for several months in response to a crisis of rare proportions. Little is known about the specific factors that affected the progression of the public’s reactions during the pandemic. Individual factors associated with pandemic-related behavior in general, and compliance with public health measures in particular, are not firmly established. We undertook a survey of behavior, emotions, reasoning style, and mental health in the province of Quebec at the beginning, the peak, and the aftermath of the first wave of the COVID-19 pandemic. We recruited 530 responders from a convenience sample; 154 responders participated in all three surveys. Emotions were most intense at the beginning of the first wave of the pandemic, not at its peak. Responders’ compliance with three public health measures decreased between the peak and the aftermath of the first wave of the pandemic; however, mask wearing also became more common. Pandemic-related behavior in general, and compliance with public health measures specifically, were predicted by avoidance-related emotions evoked by the pandemic. Approach-related emotions linked to the societal response contributed specifically to the prediction of compliance with public health measures. In contrast, reasoning style and mental health did not as consistently predict behavior during the pandemic. Our research may help inform public health policy during other waves of the COVID-19 pandemic and future global health crises.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".