Cross-Sector Partnerships and Public Health: Challenges and Opportunities for Addressing Obesity and Noncommunicable Diseases Through Engagement with the Private Sector
Bibliographic record
Abstract
Over the past few decades, cross-sector partnerships with the private sector have become an increasingly accepted practice in public health, particularly in efforts to address infectious diseases in low- and middle-income countries. Now these partnerships are becoming a popular tool in efforts to reduce and prevent obesity and the epidemic of noncommunicable diseases. Partnering with businesses presents a means to acquire resources, as well as opportunities to influence the private sector toward more healthful practices. Yet even though collaboration is a core principle of public health practice, public-private or nonprofit-private partnerships present risks and challenges that warrant specific consideration. In this article, we review the role of public health partnerships with the private sector, with a focus on efforts to address obesity and noncommunicable diseases in high-income settings. We identify key challenges-including goal alignment and conflict of interest-and consider how changes to partnership practice might address these.
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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 | 0.019 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".