Understanding Decisions to Engage in Public Health Measures (PHMs) during COVID-19
Bibliographic record
Abstract
Widespread compliance with public health measures (PHMs) has been critical to containing the COVID-19 pandemic as well as other infectious diseases. Vaccination, mask-wearing, handwashing, and physical distancing have been among most recommended PHMs for mitigating the impacts of the pandemic. To facilitate development of public health policies and campaigns to encourage compliance with PHMs, it is necessary to gain insight into factors contributing to decisions to engage in the PHMs. The four studies presented in this dissertation are focused on investigating contributions of well-established cognitive biases, delay discounting and intolerance of uncertainty, to individual’s compliance with PHMs during the COVID-19 pandemic. Approximately 7,000 participants from 13 countries were recruited for an online survey between June and August 2021. Participants completed measures of delay discounting, intolerance of uncertainty, demographics, distress level, and PHM compliance. After controlling for demographic and distress variables, delay discounting (tendency to prefer smaller immediate rewards over larger later rewards) was a negative predictor of vaccination, but a positive predictor of physical distancing and handwashing. The participants were invited to complete a follow-up study between July and August 2022 and respond to questions about engagement in protective behaviors, including vaccination status and willingness to receive a booster dose. In the sub-sample of participants who reported receiving at least one main dose of the vaccine (n=2,547), a greater tendency to discount future rewards was associated with reduced willingness to receive a booster dose, after controlling for demographic and distress variables. In the sub-sample of participants who reported no intention to get vaccinated in 2021 (n=251), an age x intolerance of uncertainty interaction predicted the likelihood of change in vaccination status a year later. Younger participants were more likely to change their mind about vaccination compared to older participants, especially if they were high on intolerance of uncertainty. The results of the four studies advance our understanding of health-related decision-making, offering insights into vaccine hesitancy, compliance with PHMs, and the cognitive processes associated with these choices during a pandemic. The implications extend to theoretical models, public health strategies, and interventions aimed at promoting widespread engagement in PHMs.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".