Bibliographic record
Abstract
lthough this issue of THE MILBANK QUARTERLY was not planned to be thematic, much of it has turned out to be about major changes in health care systems.The issue begins with "Large-System Transformation in Health Care: A Realist Review" by Allan Best, Trisha Greenhalgh, Steven Lewis, Jessie Saul, Simon Carroll, and Jennifer Bitz.The article grew out of a six-month project, Knowledge to Action for System Transformation, which was carried out for the Saskatchewan Ministry of Health.The task was to summarize what the research literature could teach about factors that affect the success of major health care reforms.The article is of interest not only for what the literature review produced but also for how it was done.The authors used the theoretical framework of "complex adaptive systems" and the "realist review" approach, which focuses on context as well as on interventions and outcomes (Pawson et al. 2005).They thus paid particular attention to (1) mechanisms and social processes that influence large-system transformations in health care, (2) important contextual factors, (3) the identification of "transition points" in large-system changes, and (4) the role of government.Best and colleagues derived five "simple rules" for increasing the likelihood of success in large-system transformations.These pertain to a blend of designated and distributed leadership, the importance of feedback loops, attention to history, engagement of physicians, and involvement of patients and families.The next two articles in this issue provide early evidence regarding two of the central components of the transformation goals built into the Patient Protection and Affordable Care Act (ACA): accountable care organizations and the patient-centered medical home.The first of these articles is "Interpretations of Integration in Early Accountable Care Organizations" by
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.307 | 0.189 |
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".