The role of private healthcare sector actors in health service delivery and financing policy processes in low-and middle-income countries: a scoping review
Bibliographic record
Abstract
The expansion of the private healthcare sector in some low-income and middle-income countries (LMICs) has raised key questions and debates regarding the governance of this sector, and the role of actors representing the sector in policy processes. Research on the role played by this sector, understood here as private hospitals, pharmacies and insurance companies, remains underdeveloped in the literature. In this paper, we present the results of a scoping review focused on synthesising scholarship on the role of private healthcare sector actors in health policy processes pertaining to health service delivery and financing in LMICs. We explore the role of organisations or groups-for example, individual companies, corporations or interest groups-representing healthcare sector actors, and use a conceptual framework of institutions, ideas, interests and networks to guide our analysis. The screening process resulted in 15 papers identified for data extraction. We found that the literature in this domain is highly interdisciplinary but nascent, with largely descriptive work and undertheorisation of policy process dynamics. Many studies described institutional mechanisms enabling private sector participation in decision-making in generic terms. Some studies reported competing institutional frameworks for particular policy areas (eg, commerce compared with health in the context of medical tourism). Private healthcare actors showed considerable heterogeneity in their organisation. Papers also referred to a range of strategies used by these actors. Finally, policy outcomes described in the cases were highly context specific and dependent on the interaction between institutions, interests, ideas and networks. Overall, our analysis suggests that the role of private healthcare actors in health policy processes in LMICs, particularly emerging industries such as hospitals, holds key insights that will be crucial to understanding and managing their role in expanding health service access.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".