Critical Success Factors for Promoting Healthy Food Environments and Healthy Eating Through Local Policy Changes: Learning From Canada
Notice bibliographique
Résumé
Background and context: Policies implemented at the local level can create healthier environments that enable individuals to engage in healthier, cancer preventive behaviors - such as healthy eating. Policies support cancer preventive behaviors in a sustainable and often cost-effective manner. Many theoretical frameworks exist to describe the policy process; however in practice, policy development is often considered a complex and unfamiliar mechanism to the cancer prevention and health promotion community. Aim: To identify and better understand the critical success factors underlying cancer prevention policy success, the Canadian Partnership Against Cancer analyzed the policy outcomes - focused on food environments and healthy eating - from their pan-Canadian funding initiative Coalitions Linking Action and Science for Prevention (CLASP). Strategy/Tactics: Four projects funded through the CLASP initiative, from 2009 to 2016, have yielded 260 policy outcomes related to improving food environments and healthy eating. The policy changes were the result of evidence-based interventions implemented at the local level (i.e., municipalities, schools/child care, and workplaces). Program/Policy process: Over 220 knowledge products and evaluation documents were reviewed to identify food environment and healthy eating policy outcomes and key lessons learned. The policy outcomes were analyzed and categorized according to: a) implementation setting (municipality, school/child care, workplace); and b) policy lever addressed. Policy lever categories were sourced from the World Cancer Research Fund's (WCRF) NOURISHING Framework. Ten key informant interviews were conducted with former project members to refine and validate the lessons learned. Lessons learned were organized into a final list of critical success factors and themed into overarching categories. Outcomes: The majority of the food environment and healthy eating policy outcomes from CLASP occurred in workplace settings (n=133) and municipalities (n=111), and the least in schools/child care settings (n=16). The most frequent NOURISHING policy lever was “Offer healthy food and set standards in public institutions and other specific settings” primarily through policies to ban the sale of energy drinks (n=83) and implementing nutrition standards (n=58). Ten critical success factors were identified and described within three categories: people (n=3); tools (n=3); and approaches and ways of working (n=4). What was learned: A key takeaway from this work was a combination of cross-sectoral partnerships, tools and evidence, and collaborative ways of working were crucial to advance food environment and healthy eating policy change in municipalities, schools and child care settings, and workplaces. By utilizing the international WCRF NOURISHING Framework, it is intended that the lessons learned from this policy work in a Canadian context can inform local-level cancer prevention policy efforts around the world.
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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 | 0,014 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,022 | 0,005 |
| Communication savante | 0,008 | 0,003 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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 ».