The Effectiveness of Partnerships With Commercial Actors to Improve Food Environments: A Systematic Review
Bibliographic record
Abstract
Partnerships with commercial actors have been proposed as a policy approach to create healthier food environments. We conducted a systematic review to assess their effectiveness for improving food environments and population health at state, national, or international levels. We searched in 14 databases and two websites for real-world evaluations published between 2010 and 2020. Study quality was appraised using a modified Newcastle-Ottawa Scale. Data were synthesized narratively by outcome (human, food environment, policy content, and implementation progress), considering their effect direction. Seventeen studies reporting on seven PPPs in four countries were included. Most studies (n = 14) involved food reformulation, especially salt reduction. Three focused on specific settings (the eating out-of-home sector, schools, and convenience stores). There was mixed evidence that partnerships make people buy fewer calories or more school meals (n = 3 studies) or reduce product sodium content (n = 6). Some positive effects were described in one uncontrolled study each for decreasing trans-fatty acid intake and for making healthier options more available in school cafeterias, but these studies had important limitations. Five document analyses highlighted shortcomings in the partnerships, including their limited scope, failure to add value to ongoing actions, varying participation levels, and lack of implementation, monitoring, and reporting. Alternative policy approaches should be considered. This systematic review is registered on PROSPERO as CRD42020170963.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.012 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".