Implementing a Rural Natural Experiment: A Protocol for Evaluating the Impacts of Food Coops on Food Consumption, Resident’s Health and Community Vitality
Bibliographic record
Abstract
BACKGROUND: Local food environments are recognized by experts as a determinant of healthy eating. Food cooperatives (coop) can promote the accessibility to healthier foods and thus improve the health of the population, particularly in remote rural communities. OBJECTIVE: To measure the effects of implementing a food coop in a disadvantaged community with poor access to food. We have two main research questions: (1). Does the establishment of a food coop in rural areas described as food deserts have an impact on accessibility, frequency of use, food consumption, food quality, and ultimately the health of individuals? (2). Does the establishment of a food coop in rural areas described as food deserts have an impact on food security and community vitality? DESIGN: A natural experiment with a mixed pre/post method will be used. The sample is composed of households that came from geographically isolated communities (population: 215 to 885 inhabitants) which qualified as food deserts and located in rural areas of Quebec (Canada). All communities plan to open a food coop (in the years 2022-2023), and as their opening will be staggered over time, participants from communities with a new food coop (intervention) will be compared to communities awaiting the opening of their food coop (control). Data collection was carried out at three time points: (1) before; (2) 1 to 5 months after; and (3) 13 to 17 months after the opening of the coop. Questionnaires were used to measure sociodemographic variables, dietary intake, residents' health, and community vitality. Semi-structured interviews were conducted with community stakeholders. RESULTS: Few natural experiments have been conducted regarding the impact of implementing food coops. Gathering concrete data on the effectiveness and processes surrounding these interventions through natural experiments will help to quantify their impact and guide knowledge users and policymakers to make more informed decisions.
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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 | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".