22.B. Workshop: Behavioural insights and public health
Bibliographic record
Abstract
Abstract Many of today's most pressing public health challenges have a strong behavioural component. Behavioural, psychosocial, and environmental factors play a major role in the development and progression of chronic diseases. Eliminating those risks would make it possible to prevent at least 80% of cardiovascular diseases, 75% of diabetes, and 40% of cancers. Behavioural insights provide an empirically informed perspective on how individuals make decisions, including the important recognition that even subtle changes in the environment can have meaningful impacts on behaviour. This workshop will provide examples from the literature and recent government initiatives that incorporate concepts from behavioural sciences in order to improve health, decision-making, and government efficiency. The examples highlight the potential for behavioural sciences to improve the effectiveness of public health policy at low cost. Although incorporating insights from behavioural sciences into public health policy has the potential to improve population health, its integration into government public health programs and policies requires careful design and continual evaluation of such interventions. Limitations and drawbacks of the approach will be discussed. The aim of this workshop is to broaden our understanding of measures that have originated from behavioural sciences and have a lot to offer to public health. This workshop also seeks to contribute to capacity building in knowledge translation and evidence-informed decision-making in public health. The workshop will consist of five presentations providing an overview of topical issues in the field of behaviour change and knowledge translation, followed by an interactive audience discussion. The first presentations will provide insights into current behaviour change theories. The second presentation will discuss the possibilities of using behaviour change principles in the development and adoption of health policies showcasing the recently adopted Canadian Association of Cardiovascular Prevention and Rehabilitation Guide and the Food Guide. The third presentation will highlight the challenges in tackling physician's ability to effectively conduct behaviour change counselling with their patients in the context of chronic disease prevention. The fourth presentation will introduce the free academic meta-search engine - Motrial, which has a great potential in evaluating the randomized controlled trials and fuelling meta-analyses and systematic reviews in return of better quality. The fifth presentation will introduce a novel WHO/Europe guide on brief interventions for NCDs risk factors. Further to the reflection on the current knowledge base, an audience discussion will give attendees the opportunity to share their opinions regarding challenges and opportunities in behaviour change and knowledge translation to improve people's health and well-being. Key messages The application of behavioural insights into public health has its opportunities and challenges. Because behavioural insights is a very promising, yet a relatively new field, the research literature remains thin, and policy can sometimes get ahead of science.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.082 | 0.053 |
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".