Evaluating a 52-week fresh food prescribing program in Ontario, Canada: A mixed-methods study on food insecurity, fruit and vegetable intake, and health
Bibliographic record
Abstract
BACKGROUND: Food insecurity is linked with suboptimal diet and comprises an important risk factor for nutrition-related chronic diseases. Fresh food 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 mixed methods study was to assess changes in food security, fruit and vegetable intake, and health among adult participants of a fresh food prescribing program in Guelph, Ontario, Canada. METHODS: A total of 57 adult participants who were experiencing food insecurity and had ≥ 1 cardio-metabolic condition or micronutrient deficiency received fresh food prescriptions from their healthcare practitioner, which included a nutrition and cooking information package and weekly vouchers ($10 per person in household) for an online produce market for 52 consecutive weeks. Pre-, mid-, and post-intervention surveys, blood pressure measurements, and clinical bloodwork were collected to assess food security, fruit and vegetable intake, self-reported health, and blood biomarkers of cardio-metabolic and nutritional health. We used a single-arm repeated-measures evaluation and paired t-tests and Fisher's exact tests to assess changes. Linear regression models were used to assess factors associated with change in fruit and vegetable intake. Semi-structured interviews were conducted with participants to expand on survey findings. Qualitative data were analyzed thematically using an inductive constant comparative approach. RESULTS: Forty-nine participants completed post-intervention data collection. The proportion of participants experiencing severe food insecurity decreased after the intervention from 38.1% to 23.8%. Intake of fruit, orange vegetables, and 'other' vegetables increased during the intervention (p < 0.05). Mean triglyceride, fasting insulin, and ascorbic acid levels improved (p < 0.05). More severe food insecurity and lower fruit and vegetable intake at baseline, as well as more frequent interaction with healthcare providers, were associated with a greater increase in fruit and vegetable intake from pre- to post-intervention (p < 0.05). In interviews, participants reported that the program increased access to fresh fruits and vegetables, improved mental and physical health, provided social connections, and reduced financial stress. CONCLUSIONS: Fresh food prescription programs may improve food security and increase fruit and vegetable intake, but further research is needed to determine their long-term health impacts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".