Early impact of a new food store intervention on health-related outcomes
Bibliographic record
Abstract
This study investigated the early impact of a community-based food intervention, the Good Food Junction (GFJ), a full-service grocery store (September 2012 - January 2016) in a former food desert in Saskatoon, Canada. The hypothesis tested was that frequent shopping at the GFJ improved food security and selected health-related outcomes among shoppers, and the impact was moderated by socioeconomic factors. Longitudinal data were collected from 156 GFJ shoppers, on three occasions: 12-, 18-, and 24-months post-opening. Participants were grouped into three categories based on the frequency of shopping at the GFJ: low, moderate, and high. A generalized estimating equations approach was used for model building; moderating effects were tested. Participants were predominantly female, Indigenous, low-income, and had high school or some post-secondary education. The GFJ use was associated with household food security (OR for high and moderate frequency shoppers reporting less than a high school education were 1.81 and 1.06, respectively), and mental health (OR for high and moderate frequency shoppers reporting high income were 2.82 and 0.87, respectively) exhibiting a dose-response relationship, and indicated that these outcomes were significantly moderated by participants' socioeconomic factors. Shopping at the GFJ had a positive effect on food security and mental health, but to varying levels for those with low incomes, with less than high school or high school or better levels of education.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".