Evaluation of frameworks demonstrating the role of private sector in non-communicable disease management and control: a systematic review and thematic synthesis
Bibliographic record
Abstract
Objectives Conduct a systematic review of existing frameworks to understand the for-profit private sector’s roles in non-communicable disease (NCD) control and management. Control includes population-level control measures that prevent NCDs and mitigate the magnitude of the NCD pandemic, and management includes treatment and management of NCDs. The for-profit private sector was defined as any private entities that make profit from their activities (ie, pharmaceutical companies, unhealthy commodity industries, distinct from not-for-profit trusts or charitable organisations). Design A systematic review and inductive thematic synthesis was performed. Comprehensive searches of PubMed, EMBASE, Cochrane Library, Web of Science, Business Source Premier and Proquest/ABI Inform were conducted on 15 January 2021. Grey literature searches were conducted on 2 February 2021 using the websites of 24 relevant organisations. Searches were filtered to only include articles published from the year 2000 onwards, in English. Articles that included frameworks, models or theories and the for-profit private sector’s role in NCD control and management were included. Two reviewers performed the screening, data extraction and quality assessment. Quality was assessed using the tool developed by Hawker et al for qualitative studies. Setting The for-profit private sector. Results There were 2148 articles initially identified. Following removal of duplicates, 1383 articles remained, and 174 articles underwent full-text screening. Thirty-one articles were included and used to develop a framework including six themes that outlined the roles that the for-profit private sector plays in NCD management and control. The themes that emerged included healthcare provision, innovation, knowledge educator, investment and financing, public–private partnerships, and governance and policy. Conclusion This study provides an updated insight on literature that explores the role of the private sector in controlling and monitoring NCDs. The findings suggest that the private sector could contribute, through various functions, to effectively manage and control NCDs globally.
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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 | 0.187 | 0.315 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.013 |
| Bibliometrics | 0.059 | 0.040 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".