Engaging Communities in Monitoring Local Food Environments: The Local Environment Action on Food (LEAF) Project
Bibliographic record
Abstract
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.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".