The role of the private sector in noncommunicable disease prevention and management in low- and middle-income countries: a series of systematic reviews and thematic syntheses
Bibliographic record
Abstract
PURPOSE: framework's pillars. METHODS: Six systematic reviews and thematic syntheses were performed between March-August 2021, Six databases, websites of relevant organizations, and references lists of included studies were comprehensively searched. Studies published in English from 2000 onwards involving the pillar of interest, for-profit private sector, NCD prevention/management, and LMIC context were included. Results were synthesized using an inductive thematic synthesis approach. RESULTS: Ultimately, 25 articles were included in the PPP review, 33 in Governance and Policy, 22 in Healthcare Provision, 15 in Innovation, 14 in Knowledge Educator, and 42 in Investment and Finance. The following themes emerged: PPPs (coordination; financial resources; provision; health promotion; capacity building; innovation; policy); Governance/Policy (lobbying; industry perception; regulation); Healthcare Provision (diagnosis/treatment; infrastructure; availability/accessibility/affordability); Innovation (product innovation; process innovation; marketing innovation; research; innovation dissemination); Knowledge Educator (training; health promotion; industry framework/guideline formation); Investment and Finance (treatment cost; regulation; private insurance; subsidization; direct investment; collaborative financing; innovative financing; research). CONCLUSION: These findings will be instrumental for LMICs considering private sector engagement. Potential conflicts of interest must be considered when implementing private sector involvement.
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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 | 0.066 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".