Stewardship of quality of care in health systems: Core functions, common pitfalls, and potential solutions
Bibliographic record
Abstract
Summary National Ministries of Health in low‐ and middle‐income countries (LMICs) have a key role to play as stewards of the quality agenda in their health systems. This paper uses a previously developed six‐point framework for stewardship (strategy formulation, intersectoral collaboration, governance and accountability, health system design, policy and regulation, and intelligence generation) and identifies specific examples of activities in LMICs in each of these domains, pitfalls to avoid, and possible solutions to these pitfalls. Many LMICs now have quality strategies with clear vision statements. There are good examples of quality agencies and donor collaboration councils to coordinate activities across different sectors. There are multiple options for accountability, including public reporting, community accountability structures, results‐based payment, accreditation, and inspection. To improve health system design, available tools include decision support tools, task‐shifting models, supply chain management, and programs to train quality improvement staff. Policy options include legislation on disclosure of adverse events, and regulations to ensure skills of health care providers. Lastly, health information tools include patient registries, facility surveys, hospital discharge abstracts, standardized population and patient surveys, and dedicated agencies for reporting on quality. Policy‐makers can use this article to identify options for driving the quality agenda and address anticipated implementation barriers.
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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 | 0.081 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 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".