Identifying barriers and facilitators in the development and implementation of government-led food environment policies: a systematic review
Bibliographic record
Abstract
CONTEXT: Policy-specific actions to improve food environments will support healthy population diets. OBJECTIVE: To identify cited barriers and facilitators to food environment policy (FEP) processes reported in the literature, exploring these according to the nature of the policy (voluntary or mandatory) and country development status. DATA SOURCES: A systematic search was conducted of 10 academic and 7 grey-literature databases, national websites, and manual searches of publication references. DATA EXTRACTION: Data on government-led FEPs, barriers, and facilitators from key informants were collected. DATA SYNTHESIS: The constant-comparison approach generated core themes for barriers and facilitators. The appraisal tool developed by Hawker et al. was adopted to determine the quality of qualitative and quantitative studies. RESULTS: A total of 142 eligible studies were identified. Industry resistance or disincentive was the most cited barrier in policy development. Technical challenges were most frequently a barrier for policy implementation. Frequently cited facilitators included resource availability or maximization, strategies in policy process, and stakeholder partnership or support. CONCLUSIONS: The findings from this study will strategically inform health-reform stakeholders about key elements of public health policy processes. More evidence is required from countries with human development indices ranging from low to high and on voluntary policies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42018115034.
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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 | 0.043 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".