Can the Healthcare System Improve Food Security? A Need for Collaborative Community Partnerships
Bibliographic record
Abstract
The COVID-19 pandemic has heightened the food insecurity crisis in Canada, and existing supports have been largely insufficient to meet the food needs of communities. In response to increasing reports of food insecurity among Toronto residents during the pandemic, the Food RX program was developed as a collaborative initiative between FoodShare Toronto - a local, community-based food justice organization - and the University Health Network, a large university-affiliated hospital network in downtown Toronto, ON. This commentary describes the Food RX program, highlights the lessons learned during its early implementation and offers a set of recommendations for building community partnerships moving forward.
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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 | 0.037 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.019 |
| Scholarly communication | 0.018 | 0.028 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.029 | 0.035 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".