Protecting patients: international trends in medical governance
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,007 |
| Communication savante | 0,009 | 0,006 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,013 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».