Efficient purchasing in public and private healthcare systems: mission impossible?
Bibliographic record
Abstract
Contents: International health care reform: what goes round, comes round The pervasive role of ideology in the optimism of the public-private mix in the public healthcare system Efficient purchasing in public and private healthcare systems: mission impossible? The public-private mix in the UK UK health care reform: continuity and change The mix of public and private payers in the American health system Political wolves and economic sheep: the sustainability of public health insurance in Canada Public-private mix for health in France The public-private mix in Scandinavia Public-private mix for health care in Germany The public-private mix in health services: New Zealand The role of the private sector in the Australian health care system Common challenges in health care markets Enduring problems in health care delivery. Contributors: Nancy Devlin, Cam Donaldson, Robert Evans, Karen Gerard, Jane Hall, L. Hartmann, Axel Olaf Kern, Rudolf Klein, Nicholas Mays, Craig Mitton, Martin Pfaff, Kjeld Pedersen, Uwe Reinhardt, Lise Rochaix, Elizabeth Savage, Alan Williams.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".