Cross national insights into pandemic preparedness from the iCARE study
Bibliographic record
Abstract
Abstract The COVID-19 pandemic has taught us much about the drivers of pandemic prevention behaviours, e.g., getting vaccinated, mask wearing, physical and social distancing. For example, women, younger individuals, and people living in rural areas, were less likely to get COVID-19 vaccines, whereas people who had previously received an influenza vaccine were more likely to get COVID-19 vaccines. In addition, vaccine hesitancy differed notably between countries, e.g., ranging from ∼10% (e.g., Brazil) to ∼50% (e.g., France). Though COVID-19 was a global pandemic, the policy responses to mobilise engagement in positive prevention behaviours varied considerably across countries, with many poorly adapted to account for the variability detailed above. Unsurprisingly, the disconnect between what populations needed and what they got has led to varying degrees of negative sentiments towards future pandemic measures. One of the key behavioural messages that came out of this was that public health interventions need to be aligned with the specific needs of certain populations. This presentation will summarize pandemic preparedness data that is currently being collected across 6 countries (Ireland, France, Italy, Canada, Australia, and Colombia) in the international COVID-19 Awareness, Responses and Evaluation (iCARE) Study (one of the largest behavioural pandemic data collection platforms in the world) to identify the factors that are associated with intentions to engage in future pandemic behaviours. This data will then be mapped onto well-established behavioural science theories and models to propose a series of inter- and intra-country interventions that should promote improved public preparedness should we need to re-engage them for future pandemics.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".