2.I. Round table: Addressing misconceptions about behavioural science in public health: setting the record straight!
Bibliographic record
Abstract
Abstract Importance Health behaviours have long been recognized as major risk factors for chronic disease (e.g., cancer, heart, lung and metabolic diseases); the COVID-19 pandemic reminded us of the critical role of behaviours in preventing virus transmission; and solving the climate crisis is going to rely on changing human behaviours related to consumption, transportation, agriculture and energy use, on a global scale. On 29 May 2023, the World Health Assembly adopted the first-ever global Behavioural Sciences for Better Health resolution (WHA76.7). This resolution requested that the World Health Organization (WHO) support the application of behavioural sciences in public health by enhancing awareness, knowledge, and evidence, and providing technical assistance, normative guidance, and capacity-building. Unfortunately, the potential for this resolution to positively affect global health may be undermined by the fact that many public health practitioners are reticent to integrate behavioural sciences into their practice and decision-making. Why? Because there are concerns that promoting the use of behavioural science in public health would somehow undermine or detract from the need to focus on regulation and policy change at the population level. Behavioural scientists disagree, and worry that this view perpetuates a ‘false dichotomy’ that is both incorrect and counter-productive. Objectives This workshop will shed empirical light on the controversies surrounding this debate by bringing together behavioural and public health scientists to share their perspectives, objectives, and concerns, with the ultimate goal of generating an action plan to promote better collaboration between behavioural science and public health. By the end of this workshop, participants will be able to: 1) Summarize the perspectives and concerns of public health scientists regarding the use of knowledge and tools from the behavioural sciences for health promotion; 2) Identify potential strategies for promoting the use of knowledge and tools from the behavioural sciences among public health scientists; Objectives will be achieved using brief panel presentations and interactive group discussions. Workshop participants will be invited to brainstorm behavioural science promotion strategies that will be discussed with the larger group. Relevance This workshop is perfectly aligned with the conference theme of ‘Investing in sustainable health and well-being’ by promoting greater and more effective use of behavioural science knowledge and tools among public health scientists and practitioners. Key messages • The world's public health priorities require changing behavioural on a global scale. • Use of behavioural science evidence and tools is essential for achieving public health objectives. Speakers/Panellists Kim Lavoie UQAM, Department of Psychology, Montreal, Canada Simon Bacon Concordia University, Montréal, Canada Iveta Nagyova EUPHA Jet Sanders RIVM, Bilthoven, Netherlands
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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 | 0.080 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.025 | 0.031 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.032 | 0.056 |
| Insufficient payload (model declined to judge) | 0.063 | 0.052 |
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".