The development of medical-manager roles in European hospital systems: a framework for comparison
Bibliographic record
Abstract
A central motif of health reforms around the world has been the drive to persuade doctors and other clinical professionals to become more actively engaged in the management of services. Examples include moves to extend the commissioning role of primary care doctors (such as general practitioners in the UK) and the introduction of ‘clinical directorates’ in secondary care. This strategy has been seen as a means of controlling professionals, turning ‘poachers into game keepers’, especially with regard to resource allocation. However, there is also a mounting body of evidence pointing to how clinical leadership may play a role in stimulating quality improvement and new innovations inservice design, with positive consequences for patient safety and satisfaction (1). Focusing on the top 100 hospitals in the US Goodall (2) finds a strong positive association between the ranked quality of hospitals and whether the chief executive officer was a clinician. A survey of 1200 hospitals across seven countries (UK, US, Germany, France, Italy,Canada and Sweden) conducted by McKinsey and LSE also finds that clinically qualified managers improve both the effectiveness of management decisions and clinical performance of hospitals overall (3).
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".