Promoting Healthy Food Access and Nutrition in Primary Care: A Systematic Scoping Review of Food Prescription Programs
Bibliographic record
Abstract
OBJECTIVE: To conduct a scoping review to synthesize evidence on food prescription programs. DATA SOURCE: A systematic search of PubMed, CINAHL, Web of Science, Embase, and the Cochrane Library was conducted using key words related to setting, interventions, and outcomes. STUDY INCLUSION AND EXCLUSION CRITERIA: Publications were eligible if they reported food prescription administered by a health care practitioner (HCP) with the explicit aim of improving healthy food access and consumption, food security (FS), or health. DATA EXTRACTION: A data charting form was used to extract relevant details on intervention characteristics, study methodology, and key findings. DATA SYNTHESIS: Study and intervention characteristics were summarized. We undertook a thematic analysis to identify and report on themes. A critical appraisal of study quality was conducted using the Mixed Methods Appraisal Tool (MMAT). RESULTS: A total of 6145 abstracts were screened and 23 manuscripts were included in the review. Food prescriptions may improve fruit and vegetable consumption and reduce food insecurity (FI). Evidence for impacts on diet-related health outcomes is limited and mixed. The overall quality of included studies was weak. Addressing barriers such as stigma, transportation, and poor nutrition literacy may increase utilization of food prescriptions. CONCLUSION: Food prescriptions are a promising health care intervention. There is a need for rigorous studies that incorporate larger sample sizes, control groups, and validated assessments of dietary intake, food security, and health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".