Can applying behaviour change principles make public health policies more impactful and sustainable?
Bibliographic record
Abstract
Abstract Issue/problem The incorporation of evidence-based recommendations into policy documents and health guidance often does not lead to measurable changes in public health outcomes. Description of the problem All the preventative measures of COVID-19 were behavioural in nature, e.g., getting vaccinated, wearing masks, physically and socially distancing, getting tested, etc. However, most government policies did not consider behavioural science or behaviour change frameworks when crafting them. This led to a significant reduction in trust in governments and a resistance to the evolving policies. Results Through the international iCARE study (www.icarestudy.com), we captured data throughout the pandemic on people's capabilities, opportunities, and motivation to engage in COVID-19 prevention behaviours, as well as their actual behaviours. This data has led to key insights into how policies could have been crafted to actively engage individuals in the varying measures that they needed to undertake to reduce the impact of COVID-19. Lessons learned The incorporation of behaviour change principles in the development and implementation of policies has the potential to engage more shareholders and drive behaviour change. However, more evidence is needed to determine the optimal way to do so, and to assess the effectiveness of these efforts in improving public health outcomes. To achieve this, a rigorous monitoring plan is necessary to evaluate the impact of policy changes and health guidance on public health outcomes.
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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 | 0.064 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".