Assessing the relationship between delay discounting and decisions to engage in various protective behaviors during COVID-19
Bibliographic record
Abstract
Abstract Research suggests that discounting of delayed rewards (i.e., tendency to choose smaller immediate rewards over large later rewards) is a promising target of intervention to encourage compliance with public health measures (PHM), such as vaccination compliance. The effects of delay discounting, however, may differ across the types of PHMs, given that the benefits of vaccination, unlike other PHMs (physical distancing, handwashing, and mask-wearing), are more temporally delayed. Here, we examined whether delay discounting predicts engaging in COVID-19 PHMs in approximately 7,000 participants recruited from 13 countries in June–August 2021. After controlling for demographic and distress variables, delay discounting was a negative predictor of vaccination, but a positive predictor of physical distancing (when restrictions are in place) and handwashing. There was no significant association between delay discounting and frequency of mask-wearing. It is possible that increasing vaccination compliance may require greater emphasis on future benefits of vaccination, whereas promotion of physical distancing and hand hygiene may require greater focus on the present moment. Further research is needed to investigate the nature of this relationship and its implications for public health messaging.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".