22.B. Workshop: Behavioural insights and public health
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,003 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,010 | 0,003 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,011 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,082 | 0,053 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».