Real Reform Begins Within: An Organizational Approach to Health Care Reform
Bibliographic record
Abstract
Health care systems are under pressure to control their increasing costs, to better adapt to evolving demands, to improve the quality and safety of care, and ultimately to ameliorate the health of their populations. This article looks at a battery of organizational options aimed at transforming health care systems and argues that more attention must be paid to reforming the delivery mechanisms that are so crucial for health care systems' overall performance. To support improvement, policies can rely on organizational assets in two ways. First, reforms can promote the creation of new organizational forms; second, they can employ organizational levers (e.g., capacity development, team-based organizations, evidence-informed practices) to achieve specific policy goals. In both cases organizational assets are mobilized with a view to creating complete health care organizations -- that is to say, organizations that have the capacity to function as high-performing systems. The challenges confronting the development of more complete health care organizations are significant. Real health care system reforms may likewise require implementing ecologies of complex innovation at the clinical, organizational, and policy levels. Policies play a determining role in shaping these new spaces for action so that day-to-day practices may change.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.042 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| 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".