Local Environment Action on Food project: impact of a community-based food environment intervention in Canada
Bibliographic record
Abstract
Children are exposed to food environments that make nutrient-poor, energy-dense food cheap, readily available and heavily marketed; all conditions with potential negative impacts on diet and health. While the need for programmes and policies that improve the status of food environments is clear, greater public support is needed for governments to act. The purpose of this qualitative collective case study was to examine if community engagement in the Local Environment Action on Food (LEAF) project, a community-based food environment intervention in Alberta, Canada, could build public support and create action to promote healthy food environments. Semi-structured interviews with a purposeful sample of 26 stakeholders from 7 communities explored LEAF's impact and stakeholder experiences creating change. Data collection and analysis were iterative, following Charmaz's constant comparative analysis strategy. Participants reported environmental and community impacts from LEAF. Notably, LEAF created a context-specific tool, a Mini Nutrition Report Card, that communities used to promote and support food environment action. Further, analysis outlined perceived barriers and facilitators to creating community-level food environment action, including level of engagement in LEAF, perceived controllability, community priorities, policy enforcement and resources. Findings from this study suggest that community-based interventions, such as LEAF, can help build community capacity and reduce existing barriers to community-level food environment action. Thus, they can provide an effective method to build public awareness, demand and action for healthier food environments.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".