7.I. Skills building seminar: Applying behavioural sciences to public health policy-making
Notice bibliographique
Résumé
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.
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 | 0,016 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,010 | 0,015 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,069 | 0,046 |
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 ».