7.I. Skills building seminar: Applying behavioural sciences to public health policy-making
Bibliographic record
Abstract
Abstract The complexity of health issues, ranging from infectious diseases to chronic illnesses, calls for effective public health policies to improve health outcomes. Almost all public health strategies involve encouraging behaviour change. However, policy development and adoption often fail to consider the behaviour change principles that influence individuals’ decisions and actions related to health. This omission can lead to policies that are not feasible, acceptable, effective, sustainable, or equitable. Applying behaviour change principles in health policy development and adoption can lead to more effective policies that promote health behaviour change at the individual, community, and population levels. Examples of behaviour change principles include using evidence-based techniques to encourage healthy behaviours, addressing social determinants of health, and leveraging social networks. Also, communication between scientists and policy makers, including using effective communication tools such as policy briefs, plays an important role in informing political decision-making and creating impact for researchers. Incorporating behaviour change principles in health policy development and adoption require interdisciplinary collaboration, engagement with stakeholders, and attention to the cultural and social context. Aim This skills-building seminar seeks to contribute to capacity building in knowledge translation and evidence-informed decision-making in public health applying behavioural insights. More specifically, it will tackle two main questions: 1. What public health researchers need to know to impact policy? 2. How can using behaviour change principles in health policy help to bridge the implementation gap? Workshop structure This workshop will consist of two parts. In the first part, three presentations will set the scene. The first presentation will introduce the most recent advancements and future perspectives in applying behavioural insights and sciences to public health policy-making from the WHO perspective. The second presentation will highlight how behaviour change principles were used for the development and adoption of health policies in Canada. The third presentation will deal with behavioural insights for more effective communication between academics and policy makers, including a practical guide to develop effective and high-quality policy briefs. This will be followed by a reflections from representatives of academia/advisory bodies (Prof. Kim Lavoie, Co-Director, Montreal Behavioural Medicine Centre, Canada and Canada's COVID-19 Expert Advisory Panel) and WHO/Europe (Dr. Katrine Bach-Habersaat; Regional Advisor for Behavioural and Cultural Insights). Further to the reflection on the current knowledge base a structured interactive world-café methodology will be used to explore attendees’ opinions regarding the challenges and opportunities in public health policy-making to improve people's health and well-being. Key messages • Viewing policy development and adoption through the lens of behaviour change theory can help improve the effectiveness of policies and increase their impact. • By applying behaviour change principles, policymakers can better understand the motivations, barriers, and enablers of different stakeholders and tailor their policies accordingly.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.069 | 0.046 |
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".