A 52-week fresh food prescribing program reduces food insecurity and improves fruit and vegetable consumption in Ontario, Canada
Bibliographic record
Abstract
Abstract Background: Food insecurity is linked with suboptimal diet and comprises an important risk factor for nutrition-related chronic diseases. Fruit and vegetable prescription programs are designed to improve access to healthy foods, but there is limited evidence on the impacts of such programs in the Canadian context. The objective of this study was to assess changes in food security, food consumption, and health among adult participants of a fresh food prescribing program in Guelph, Ontario, Canada. Methods: A total of 57 food insecure individuals with ≥ 1 cardio-metabolic condition or micronutrient deficiency received fresh food prescriptions from their healthcare practitioner and received weekly vouchers for an online produce market. We used a single-arm repeated-measures evaluation and paired t-tests to assess changes in food security, food intake, self-reported health, and blood biomarkers of cardio-metabolic, and nutritional health. Linear regression models were used to assess factors associated with change in fruit and vegetable consumption and voucher usage. Results: Food insecurity improved following the proportion of participants classified as severely food insecure fell from 47.4–24.5%. Consumption of fruit, dark green vegetables, orange vegetables, and other vegetables increased during the intervention (p < 0.05). Mean fasting insulin and ascorbic acid levels improved (p < 0.05). Worse food insecurity and lower fruit and vegetable consumption at baseline, as well as more frequent interaction with healthcare providers, were associated with a greater increase in fruit and vegetable consumption from pre- to post-intervention (p < 0.05). Conclusions: Fruit and vegetable prescription programs may improve food security and increase fruit and vegetable consumption, but further research is needed to determine their long-term health impacts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".