Food is medicine programs for pregnant women in the United States: a systematic review
Bibliographic record
Abstract
BACKGROUND: Approximately 12.5% of households with children in the United States are food insecure. As national priorities evolve to address food insecurity, food is medicine (FIM) programs may be a part of the solution. However, there is a gap in evidence on the maternal and birth outcomes of FIM programs. PURPOSE: The goal of this systematic review was to understand the overall public health impacts of FIM programs for pregnant populations. METHODS: This systematic review was conducted in accordance with PRISMA guidelines. A search strategy was used to locate peer-reviewed literature through EBSCOhost and PubMed, and grey literature (e.g. websites, reports, booklets, and presentations) through a custom Google search in October 2022 and again in October 2024. Sources were independently screened by two researchers. Data were extracted independently by two researchers according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. RESULTS: Nine peer-reviewed and 20 grey literature programs met inclusion criteria. Limited data made it difficult to determine FIM program reach (demographics) or maintenance. Effectiveness outcomes included fruit and vegetable intake, food security, and birth outcomes. Programs were adopted by healthcare providers across all regions of the United States. The core provisions and components implemented included fruits and vegetables or ready-to-eat meals, which were provided through vouchers, coupons, or prepackaged boxes. CONCLUSIONS: This review offers a timely summary of FIM programs for pregnant women. Future research should focus on consistent reporting of measures and metrics. Additionally, longer-term studies are needed to build evidence for program sustainability.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".