Conceptual frameworks for health systems performance: a quest for effectiveness, quality, and improvement
Bibliographic record
Abstract
ISSUES: Countries and international organizations have recently renewed their interest in how health systems perform. This has led to the development of performance indicators for monitoring, assessing, and managing health systems to achieve effectiveness, equity, efficiency, and quality. Although the indicators populate conceptual frameworks, it is often not very clear just what the underlying concepts might be or how effectiveness is conceptualized and measured. Furthermore, there is a gap in the knowledge of how the resultant performance data are used to stimulate improvement and to ensure health care quality. ADDRESSING THE ISSUES: This paper therefore explores, individually, the conceptual bases, effectiveness and its indicators, as well as the quality improvement dynamics of the performance frameworks of the UK, Canada, Australia, US, World Health Organization, and Organisation for Economic Co-operation and Development. RESULTS: We see that they all conceive health and health system performance in one or more supportive frameworks, but differ in concepts and operations. Effectiveness often implies, nationally, the achievement of high quality outcomes of care, or internationally, the efficient achievement of system objectives, or both. Its indicators are therefore mainly outcome and, less so, process measures. The frameworks are linked to a combination of tools and initiatives to stimulate and manage performance and quality improvement. CONCLUSIONS: These dynamics may ensure the proper environment for these conceptual frameworks where, alongside objectives such as equity and efficiency, effectiveness (therefore, quality) becomes the core of health systems 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 | 0.096 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.006 | 0.073 |
| Scholarly communication | 0.030 | 0.033 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.018 |
| 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".