Protecting patients: international trends in medical governance
Bibliographic record
Abstract
Introduction The regulation of professional work in healthcare has become tighter as states aim to provide cost-effective, high-quality health services for citizens. This is particularly true in the case of medical professionals whose clinical decisions generate substantial healthcare spending. In consequence, a variety of institutions, rules and regulations have developed across countries that aim to control and shape the decision making of physicians. Although the aims are similar, the mechanisms differ as they are shaped by history and culture. The extent of professional selfgovernment, the range of functions that come within the scope of self-governance and the institutional regulators vary between health systems. In this chapter the focus is on the role played by professional regulators in governing their own activities. The emphasis here is on the medical profession although reforms also affect other health professions. This is either because they are included in reforming legislation, or there is a trickle-down effect as other professions follow the medical model. A key question for policy makers has been how to hold the health professions accountable for achieving good-quality care. For professional regulators, key questions have been how to maintain the continuing competence of professionals throughout their careers and how to identify poor performance early in order to protect the public. This chapter considers the trends in professional governance in order to identify the similarities and differences in addressing these key questions. It draws on a research study (Allsop and Jones, 2006a) that contributed to a wider review of professional governance in the UK following the final report of the Shipman Inquiry (2005) into how former general practitioner Harold Shipman was able to murder over 200 of his patients. The review undertaken by the Chief Medical Officer recommended radical changes in professional governance in the UK that are currently being implemented (DH, 2007). The research study looked at New South Wales, Australia; Ontario, Canada; Finland; France; the Netherlands; New Zealand; and New York State in the United States (US). These case studies were selected on two criteria: the countries had different forms of health system and were identified as being at the forefront of innovation in the literature. For non- English-speaking countries, experts were identified to contribute to our review.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".