The Effect of Individual Preferences on Precautionary Behaviors in Vaccine Taking, Saving, and Physical Activity
Bibliographic record
Abstract
The COVID-19 pandemic has underscored the importance of how people react behaviorally to external threats. Precautionary behavioral responses to COVID-19 become apparent. In addition, individual risk and time preferences are related to economic behaviors under uncertainty and health-related behaviors. This study aims to determine whether and how time and risk choices influence precautionary behaviors in vaccine-taking, saving, and physical activity during the coronavirus lockdown. We conducted a cross-sectional study utilizing an online survey, which included a sample of 1016 individuals aged 18 to 60 residing and working in Shanghai. We use logistic regressions to estimate. We have three findings. First, risk-taking and future-oriented individuals are more likely to get vaccinated. Second, future-oriented ones are more inclined to exercise at home via digital media during the lockdown. Third, neither risk preference nor time preference is predictive of precautionary saving. This work aids the literature by documenting time and risk preferences influencing health-related behaviors and life well-being during the lockdown. The conclusions have practical implications from a policy perspective.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".