Public‐private partnership (PPP) development: Toward building a PPP framework for healthy eating
Bibliographic record
Abstract
Public-private partnerships (PPPs) in public health have gained great attention in the global health literature over the last two decades. Evidence suggests that PPPs could contribute to mitigating complex health problems. There is, however, limited knowledge about the process and specific conditions in which PPPs for healthy eating, in particular, can be developed successfully. To address this gap, this article first summarizes the literature, and second, using qualitative content analysis, identifies factors deemed to influence the process of building PPPs for healthy eating. The literature search was undertaken in two stages. The first stage focused on PPPs in public health to understand what constitutes a PPP, and the types and characteristics of PPPs. The second stage sought empirical examples and conceptual papers related to PPPs for healthy eating to identify critical elements that could facilitate or hinder partnerships between the government and the food industry. The search yielded 38 articles on PPPs in public health and 20 on PPPs for healthy eating. The analysis generated 23 individual elements that have the potential to influence a successful process of building PPPs for healthy eating (eg, endorsement from an individual champion, equal representation from partner organizations on board committees). The analysis also yielded five factors that appeared to well-represent the 23 individual elements of PPP formation: motivation, enablers, governance, benefits, and barriers. These results constitute an important step to understand critical factors involved in the formation of PPPs in public health and should inform additional empirical research to validate them.
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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 | 0.074 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".