Healthy Food Procurement Policies and Their Impact
Bibliographic record
Abstract
Unhealthy eating is the leading risk for death and disability globally. As a result, the World Health Organization (WHO) has called for population health interventions. One of the proposed interventions is to ensure healthy foods are available by implementing healthy food procurement policies. The objective of this systematic review was to evaluate the evidence base assessing the impact of such policies. A comprehensive review was conducted by searching PubMed and Medline for policies that had been implemented and evaluated the impact of food purchases, food consumption, and behaviors towards healthy foods. Thirty-four studies were identified and found to be effective at increasing the availability and purchases of healthy food and decreasing purchases of unhealthy food. Most policies also had other components such as education, price reductions, and health interventions. The multiple gaps in research identified by this review suggest that additional research and ongoing evaluation of food procurement programs is required. Implementation of healthy food procurement policies in schools, worksites, hospitals, care homes, correctional facilities, government institutions, and remote communities increase markers of healthy eating. Prior or simultaneous implementation of ancillary education about healthy eating, and rationale for the policy may be critical success factors and additional research is needed.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".