The International Assessment of COVID-19-related Attitudes, Concerns, Responses and Impacts in Relation to Public Health Policies (iCARE) study
Bibliographic record
Abstract
Abstract Issue/problem COVID-19 global pandemic has had significant health, social and financial impacts on global communities. Many of the national mitigation strategies to curb the virus spread have been targeting human behaviours. Insights from behavioural sciences are crucial for effective community engagement and positive behaviour change on a population-level. Description of the study The iCARE Study (which is co-funded by CIHR and the Quebec government) is a multi-wave, international observational study of public awareness, attitudes, concerns and behavioural responses to public health policies implemented to reduce the spread of COVID-19 (www.icarestudy.com). Survey data is being continuously collected in 6 week rounds (since March 2020) using representative sampling (in Canada, Italy, Israel, France, Ireland, Australia, Colombia etc.) and convenience sampling methodology (globally). The survey was constructed around the: (1) The COM-B (Capability, Opportunity, Motivation-Behaviour) model; and the (2) The Health Beliefs Model, which provide a framework for understanding factors that predict behaviour change on a population level. Results The iCARE study leverages an existing online assessment platform, and has received over 90,000 survey responses to date. This research initiative provides real-time evidence to support COVID-19 policy strategy and communication around the world, through a global multi-stakeholder engagement that gathers expertise from almost 200 collaborators in 40 countries. Of note, the study has received a range of local, national and international media coverage and directly inputted into several government policies. Lessons iCARE study is an example of a successful intersectorial, collaborative research and policy initiative that leverages multiple stakeholders to bring behavioural perspective at the forefront of the development and delivery of public health policies globally.
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 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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".