Produce Prescriptions for At-Risk Pediatric Populations in the United States: A Systematic Review of Observational Studies and Analysis of Effect Size
Bibliographic record
Abstract
This study examined the efficacy of pediatric Produce Prescription Programs (PPP) on food security status, dietary intake, and health outcomes among children, and aimed to determine the optimal prescription dosage and exposure duration required to promote beneficial outcomes. A systematic review of studies published within the past 10 years, reporting on discrete food security status, dietary quality, and health outcomes among children was conducted. Studies not reporting child-specific data or not published in the English language were excluded. Three databases were searched (PubMed, CINAHL Complete, and EBSCO), data was narratively compiled, and the Newcastle-Ottawa Quality Assessment was employed to assess risk of bias. Prescription monetary amounts (dosages) were standardized, facilitating comparison between programs and outcomes. Nine studies (n = 3808 at-risk children) conducted at 52 sites were retrieved. Program protocols varied. Participation improved food security and fruit/vegetable intake; some beneficial changes were similar regardless of produce dosage and exposure. Data suggest conjecturally that a minimum dosage of $70/month adjusted for locality, cost-of-living and implementation year and exposure of ≥6 months might promote achievement of FV recommended guidelines. The value of educational components emerged in the studies. The findings of this study are limited by the high risk of bias embedded in the included interventions, as well as high heterogeneity amongst the programs. More research on program designs, the impact of PPPs on health outcomes, and cost-benefit analyses are warranted. Rigorous study designs are needed to assess the health impacts and long-term efficacy of pediatric PPPs.
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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 | 0.033 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".