Factors Influencing the Implementation of Nutrition Policies in Schools: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Although school nutrition policies (SNPs) have been highlighted as an important intervention to support childhood nutrition, their implementation and maintenance within real-word settings is complex. There is a need to understand the factors that influence implementation by consolidating existing research and identifying commonalities and differences. AIMS: The purpose of this review is to determine what is known about the influence of broad and local system factors on the implementation of SNPs internationally. METHOD: This scoping review involved identifying and selecting relevant literature that related SNP implementation in primary and secondary schools. Following the search process, 2,368 articles were screened and 59 articles were synthesized and charted and emerging themes were identified. RESULTS: Across the final studies identified, factors emerged as barriers and facilitators to the implementation of SNPs, with system implications that related to five areas to support policy action: providing macro-level support may encourage policy implementation; addressing the financial implications of healthy food access; aligning nutrition and core school priorities; developing a common purpose and responsibility among stakeholders; recognition of school and community characteristics. DISCUSSION: While SNPs can help to support childhood nutrition, strategies to address issues related to policy implementation need to be taken to help schools overcome persistent challenges. CONCLUSION: The results of this review provide opportunities for action across multiple system levels to ensure synergy and coordinated action toward SNP goals to foster the creation supportive nutrition environments for children.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".