Bibliographic record
Abstract
PURPOSE: This paper aims to identify variation in the introduction of New Public Management reforms in healthcare and how this variation is related to country-specific healthcare states. DESIGN/METHODOLOGY/APPROACH: The analysis uses the introduction of clinical standards in Britain and Germany as cases. The two countries are characterised by interesting differences in relation to the institutional set-up of healthcare states and as such present ideal cases to explore the specific ways of how healthcare states filter clinical standards as tools of a generic managerialism. FINDINGS: Both countries have introduced clinical standards but, importantly, the substantive nature of clinical standards differs, reflecting differences in initial institutional conditions. More specifically, in Britain clinical standards have taken the form of two parallel policies, which strengthen hierarchy-based governing and redefine professional self-regulation. In Germany, by contrast, clinical standards come in one single policy, which strengthens the hybrid of network- and hierarchy-based governing and to some extent also pure hierarchy-based forms of governing. ORIGINALITY/VALUE: First, with its cross-country comparative focus, the analysis is able to identify systematic variations across healthcare states and the specific ways in which they impact on the introduction of New Public Management. Second, with its focus on clinical standards, the analysis deals with the governance of medical practice as one of the central areas of healthcare states.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".