Towards a harmonised framework for developing quality of care indicators for global health: a scoping review of existing conceptual and methodological practices
Bibliographic record
Abstract
OBJECTIVES: Despite significant advances in the science of quality of care measurement over the last decade, approaches to developing quality of care indicators for global health priorities are not clearly defined. We conducted a scoping review of concepts and methods used to develop quality of healthcare indicators to better inform ongoing efforts towards a more harmonised approach to quality of care indicator development in global health. METHODS: We conducted a systematic search of electronic databases, grey literature and references for articles on developing quality of care indicators for routine monitoring in all healthcare settings and populations, published in English between 2010 and 2020. We used well-established methods for article screening and selection, data extraction and management. Results were summarised using a descriptive analysis and a narrative synthesis. RESULTS: The 221 selected articles were largely from high-income settings (89%), particularly the USA (46%), Canada (9%), UK (9%) and Europe (17%). Quality of care indicators were developed mainly for healthcare providers (56%), for benchmarking or quality assurance (37%) and quality improvement (29%), in hospitals (32%) and primary care (26%), across many diseases. The terms 'quality indicator' and 'quality measure' were the most frequently encountered terms (50% and 21%, respectively). Systematic approaches for quality of care indicator development emerged within national quality of care systems or through cross-country collaborations in high-income settings. Maternal, neonatal and child health (33%), mental health (26%) and primary care (57%) studies applied most components of systematic approaches, but not consistently or rigorously. DISCUSSION: The current evidence shows variations in concepts and approaches to developing quality of care indicators, with development and application mainly in high-income countries. CONCLUSION: Additional efforts are needed to propose 'best-practice' conceptual frameworks and methods for developing quality of care indicators to improve their utility in global health measurement.
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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 | 0.503 | 0.572 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.086 | 0.076 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.030 | 0.028 |
| Open science | 0.011 | 0.021 |
| Research integrity | 0.010 | 0.012 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".