Collecting behavioural data across countries during pandemics: Development of the COVID-19 Risk Assessment Tool
Bibliographic record
Abstract
Tools that can be used to collect behavioural data during pandemics are needed to inform policy and practice. The objective of this project was to develop the Your COVID-19 Risk tool in response to the global spread of COVID-19, aiming to promote health behaviour change. We developed an online resource based on key behavioural evidence-based risk factors related to contracting and spreading COVID-19. This tool allows for assessing risk and provides instant support to protect individuals from infection. The Risk Estimation Questions assessed users' location, age, gender, work environment, day-to-day behaviours currently performed, and conditions under which these behaviours would change. Users were also asked to estimate how often they keep their distance from others in public and regularly wash their hands, and the procedures they follow to do so. A multidisciplinary research team of more than 150 international experts developed the tool. Over 60,000 users in more than 150 countries have assessed their risk and provided data. The majority of respondents reported that they almost always keep their distance from others in public places, and most participants reported washing their hands after touching public or shared surfaces or when entering buildings. The tool, data, and results were openly shared to support government and health agencies developing behaviour change interventions. This tool creates a blueprint for similar digital infrastructure that can be replicated and used in future pandemics.
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 | 0.045 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".