Engaging Communities in Monitoring Local Food Environments: The Local Environment Action on Food (LEAF) Project
Notice bibliographique
Résumé
Background: Children are increasingly exposed to food environments that have negative impacts on their diet and health. While the importance of creating and implementing programs and policies that change the collective determinants of eating behaviour is clear, how to achieve this goal remains unclear. Although some public support for food environment policies and programs exists in Canada, there is still a lack of public pressure for governments to act. Evidence supports the use of interventions that involve whole communities, use multi-level strategies, and consider multiple settings to promote healthy eating. Aligned with this approach is the Local Environment Action on Food (LEAF) project, a community-based health promotion intervention that aims to stimulate local action in changing food environments by engaging stakeholders in collecting local data and developing context-specific recommendations.Research Purpose and Questions: This research explores how engaging communities in collecting and reporting on food environment data could potentially create action to promote healthy food environments. This research project addressed two overarching goals. Goal one addressed the LEAF process and was guided by the following research questions: 1. What are stakeholders’ experiences of collecting and reporting on local food environment data? 2. What are the perceived barriers and facilitators to LEAF implementation and the LEAF process? Goal two addressed action for change and was guided by the following research questions: 3. If and how does LEAF and locally driven recommendations stimulate local action for change towards environments that support healthy eating? 4. What are the perceived barriers and facilitators to LEAF’s success and sustainability?Methods: A qualitative collective case study design using semi-structured interviews with a sample of 26 stakeholders explored LEAF stakeholder experiences of collecting food environment data and creating change. Document review and participant observation aided in contextualization of interview data for goal one. Data collection and analysis were iterative, following Charmaz’s constant comparative analysis strategy.Results: Exploring goal one revealed two main themes: building and maintaining relationships and process factors that influenced LEAF and relationship building. Results suggested that a strengths-based approach to benchmarking food environments could prove beneficial. Furthermore, resulting themes provided support for the need for adaptable community interventions and demonstrated the importance of community context to intervention implementation. Exploring goal two revealed that LEAF had environmental and non-environmental impacts. Notably, LEAF created a context specific tool, a Mini Nutrition Report Card, that communities used to promote and support food environment action. Action was represented by the overarching theme opening doors and continuing conversations, which encompassed the diverse ways that LEAF stakeholders used their Mini-NRC. Further, analysis outlined perceived barriers and facilitators to creating food environment action at the community level, including level of engagement, perceived controllability, community priorities, policy enforcement, resources, and key champions.Conclusions: Findings from this research support the use of community engagement in both food environment assessments and in health promotion interventions. This research has implications for research, practice, and policy. To promote sustainability of local food environment action, we recommend the creation of a web application to enable independent community food environment assessments and a communication network to allow communities to share challenges, successes, and resources relevant to creating healthy food environments. Furthermore, we suggest the availability of financial resources allotted for policy influencers and health professionals to participate in community-based projects such as LEAF.
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 | 0,017 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,007 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,013 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 ».