Definition and key concepts of high-performing health systems: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To determine how high performing is defined in relation to a health system and chart the literature on the definitions and key concepts of high-performing healthcare systems. DESIGN: Scoping review. DATA SOURCES: MEDLINE, EMBASE, CINAHL and the Cochrane Central Register of Controlled Trials were searched from inception to July 2024. The grey literature was also searched. ELIGIBILITY CRITERIA: Included studies reported on health systems and high performance to identify explicit definitions, research outcomes and knowledge gaps. RESULTS: Two reviewers independently screened 5721 citations and 507 full-text articles, resulting in the inclusion of 35 primary articles and 47 companion documents in the review. Three independent definitions for a high-performance health system were identified. 24 research studies reported outcomes on the elements of a high-performing health system (58%), system evaluation (32%) and tool development or validation (10%). Knowledge gaps identified were the lack of a common definition, a lack of common indicators, strategies for moving evidence into policy and practice, and difficulties with comparisons across health systems. CONCLUSIONS: We found limited definitions and a lack of empirical evidence on our topic. There is an opportunity for primary research in the area of health systems and high performance.
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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 | 0.032 | 0.106 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.038 | 0.035 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| 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".