Food prescribing in Canada: evidence, critiques and opportunities
Bibliographic record
Abstract
INTRODUCTION: There is growing interest in food prescriptions, which leverage health care settings to provide patients access to healthy foods through vouchers or food boxes. In this commentary, we draw on our experiences and interest in food prescribing to provide a summary of the current evidence on this intervention model and critically assess its limitations and opportunities. RATIONALE: Food insecurity is an important determinant of health and is associated with compromised dietary adequacy, higher rates of chronic diseases, and higher health service utilization and costs. Aligning with recent discourse on social prescribing and "food is medicine" approaches, food prescribing can empower health care providers to link patients with supports to improve food access and limit barriers to healthy diets. Food prescribing has been shown to improve fruit and vegetable intake and household food insecurity, although impacts on health outcomes are inconclusive. Research on food prescribing in the Canadian context is limited and there is a need to establish evidence of effectiveness and best practices. CONCLUSION: As food prescribing continues to gain traction in Canada, there is a need to assess the effectiveness, cost-efficiency, limitations and potential paternalism of this intervention model. Further, it is necessary to assess how food prescribing fits into broader social welfare systems that aim to address the underlying determinants of food insecurity.
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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 | 0.048 | 0.195 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".