Common patterns and drivers of healthcare system reforms across the OECD nations
Bibliographic record
Abstract
Abstract Background Changing health care needs, growing public expectations for high quality and cost-effective care, a burned out and strained healthcare workforce, and market competitions are pushing countries to re-evaluate the efficiency, sustainability, and performance of their health systems. The aims of this study were to: 1) identify the prominent drivers of reforms and 2) examine the common patterns of health system reforms across multiple dimensions.Methods We conducted a targeted search of grey and peer-reviewed literature focusing on health system reforms across the 38 Organization for Economic Cooperation and Development (OECD) member countries. A thematic framework was developed by adapting an existing health system framework expanded to five dimensions: 1) financing; 2) population coverage; 3) governance; 4) service delivery; and 5) service coverage. Data were indexed and charted deductively by a single reviewer (NVivo 12).Results Reforms were most often undertaken at times of economic crises (e.g., recession, war), changes to the nation’s political climate (e.g., change in political party or system), or a drastic change in population needs (e.g., aging population, epidemic). Despite a variety in evolutionary paths to present day health systems, some common patterns emerged across the five dimensions with reform continual in most nations.Conclusion Health system reforms have historically been driven by the economic, political, and social context; a context similar to the current one. Therefore, policy makers could leverage the current context to call for structural reform to healthcare systems.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".