Food is Medicine National Summit: Transforming Health Care
Bibliographic record
Abstract
Food is Medicine (FIM) interventions reflect the critical links between food security, nutrition security, health, and health equity, integrated into health care delivery. They comprise programs that provide nutritionally tailored food, free of charge or at a discount, to support disease management, disease prevention, or optimal health, linked to the health care system as part of a patient's treatment plan. Such programs often prioritize health equity. On 26-27 April, 2023, Tufts University's Gerald J. and Dorothy R. Friedman School of Nutrition Science and Policy and Food & Nutrition Innovation Institute held a 2-day National Food is Medicine Summit with leaders, practitioners, and individuals with diverse lived experiences in health care, research, government, advocacy, philanthropy, and the private sector to identify challenges and opportunities to sustainably incorporate FIM services into the health care system and at scale. This report of a meeting describes key themes of the Summit, based on presentations and discussions on momentum around FIM, incorporating FIM in health care, tradeoffs and unintended consequences of various FIM models, scaling of programs, financing and payment mechanisms, educating and engaging the health care workforce, and federal and state government actions and opportunities on FIM. Speakers highlighted examples of recent public and private sector actions on FIM and innovative cross-sector partnerships, including state Medicaid waivers, academic and philanthropic research initiatives, health care system screenings and interventions, and collaborations including community-based organizations and/or entities outside of the food and health care sectors. Challenges and opportunities to broader implementation and scaling of FIM programs identified include incorporating FIM into health care business models, educating the health care workforce, and sustainably scaling FIM programs while leveraging the local connections of community-based organizations. This meeting report highlights recent advances, best practices, challenges, and opportunities discussed at the National Summit to inform future actions on FIM.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".