Definition and key concepts of high performing health systems: a scoping review
Bibliographic record
Abstract
Abstract Background: The COVID-19 pandemic identified the need to transform health systems globally. The meaning of a high performing health system is often shaped by specific priorities that may not be widely shared. The first step is to determine how high performing is defined in relation to a health system. The objective of this study is to chart the literature on the definitions and key concepts of high performing health care systems. Methods: A scoping review was conducted by searching the published and unpublished literature. Two reviewers independently screened titles and abstracts, then full-text articles. Data abstraction was performed independently by two investigators. Data were summarized descriptively by allocating concepts or characteristics into categories and reporting frequencies. Results: A total of 3441 citations and 485 full-text articles were screened independently by two reviewers, and we included 31 primary articles and 38 companion documents in the review. Three independent definitions for a high performance health system were identified. Eighteen research studies reported outcomes on the elements of a high performing health system (56%), system evaluation (33%), and tool development or validation (11%). 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. Scoping review registration: https://osf.io/hdyrq
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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 | 0.052 | 0.188 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.057 | 0.057 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".