“I was eating more fruits and veggies than I have in years”: a mixed methods evaluation of a fresh food prescription intervention
Bibliographic record
Abstract
BACKGROUND: Food insecurity is associated with poor nutritional health outcomes. Prescribing fresh fruits and vegetables in healthcare settings may be an opportunity to link patients with community supports to promote healthy diets and improve food security. This mixed methods study evaluated the impacts of a fresh food prescription pilot program. METHODS: The study took place at two Community Health Centre locations in Guelph, Ontario, Canada. Sixty food insecure patients with ≥1 cardio-metabolic condition or micronutrient deficiency participated in the intervention. Participants were prescribed 12 weekly vouchers to Community Food Markets. We conducted a one-group pre-post mixed-methods evaluation to assess changes in fruit and vegetable intake, self-reported health, food security, and perceived food environments. Surveys were conducted at baseline and follow-up and semi-structured interviews with participants were conducted following the intervention. RESULTS: Food security and fruit and vegetable consumption improved following the intervention. Food security scores increased by 1.6 points, on average (p < 0.001). Consumption of fruits and 'other' vegetables (cucumber, celery, cabbage, cauliflower, squashes, and vegetable juice) increased from baseline to follow-up (p < 0.05). No changes in self-reported physical or mental health were observed. Qualitative data suggested that the intervention benefited the availability, accessibility, affordability, acceptability, and accommodation of healthy foods for participating households. CONCLUSIONS: Fresh food prescription programs may be a useful model for healthcare providers to improve patients' food environments, healthy food consumption, and food security.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".