From behavioural data to policy: How iCARE informed Canada’s pandemic preparedness response
Bibliographic record
Abstract
Abstract In the context of the COVID-19 pandemic, insights from the behavioural sciences have been shown to inform policy and messaging strategies related to infection control, which has helped alleviate potential harms associated with infection control policies and strengthened our resilience to develop adaptive patterns of behaviour during health emergencies. The international COVID-19 Awareness, Responses and Evaluation (iCARE) Study (one of the largest behavioural pandemic data collection platforms in the world), leverages frameworks, evidence and tools from the behavioural sciences to inform policy design and implementation, with the goal of addressing inequities and optimising policy acceptability, uptake and effectiveness. This presentation will share how iCARE partnered with the Public Health Agency of Canada (PHAC) to collect real-time behavioural response data over the course of the pandemic, and how it was used to inform policy strategies around vaccination, testing, and prevention behaviours (e.g., isolation, mask wearing).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".