Global comparison of mitigation and vaccine behaviours, motivators, and communication
Bibliographic record
Abstract
Abstract Issue/problem In an unprecedented effort to end the pandemic, national mitigations strategies have been implemented and highly effective vaccines have been developed and rolled out in record time. Success of many of these public health endeavors is contingent upon people being willing to engage in proposed behaviours. Description of the problem The key to slowing the spread of COVID-19 and ending the pandemic, is public adherence to the rapidly evolving behaviour-based government policies. However, adherence to these policies involves implementing behaviour changes that may come with significant personal, social and economic costs. In addition, public health vaccination campaigns are widely available, and the success of these initiatives is dependent on the behaviour of people getting the vaccine. Understanding the determinants of adherence at each phase of the infection curve around the world is critical for effective policy planning and communication. Results The results of the global iCARE study demonstrated a substantial level of national variations, when it comes to engaging in preventive behaviours (social distancing, mask-wearing, hand-washing, self-isolating). Moreover, we observed increasing levels of COVID-19 vaccine hesitancy across several countries. Adherence to these behaviours seems to be driven by socio-demographic (e.g., age, sex, gender, ethnicity, parental status, employment/ student status, built environment, healthcare system factors), psychological (e.g., COVID-19 attitudes, beliefs and concerns), behavioural, physical/mental health, and economic factors. Lessons iCARE study offers deeper understanding and continuous assessment of the predictors of behavioural adherence globally. To this end, the study identified possible communication targets, and proposes tailoring the format and the content of the communication, which should be consistent, prioritize equity, foster transparency and trust-building in different communities.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".