Professional Governance Systems that Work: Managing risks and ensuring regulatory effectiveness in a global and digital age.
Notice bibliographique
Résumé
Many health professionals are governed by regulatory bodies, established through legislation to ensure that these workers practice competently and safely to serve the needs of their patients/clients and communities, and ultimately contribute to overall public health and societal well-being. As our healthcare systems struggle in the post-pandemic era to meet the high demand for healthcare services, policymakers in Canada, the United Kingdom (UK) and other countries have sought solutions that frequently have implications for the healthcare professional workforce. These solutions include greater reliance on internationally educated healthcare workers and increased adoption of artificial intelligence (AI) and related technologies to increase workforce productivity. Identifying leading governance practices in these areas to inform policymaking, however, is a challenge, and it is not yet clear how regulatory effectiveness can be determined. This knowledge synthesis project seeks to inform policymaking in both Canada and the UK by exploring what regulatory risks are raised by international migration of healthcare practitioners and digital technologies, and how these risks can be addressed in an effective manner that builds public confidence in professional regulation. Goals and Objectives This knowledge synthesis will examine research and policy to inform policymaking in both Canada and the UK, guided by our collaborators in the field of professional regulation. This project is aligned with the overarching funding call focus on envisioning governance systems that work, and also several sub-themes including digital governance, health governance, and international governance. Our core objectives are to answer the following research questions: (1) What regulatory risks are raised by international migration of healthcare practitioners and digitization? (2) Are these risks relevant to everyone or for some groups more than others? (3) How can regulatory effectiveness in these areas be identified, demonstrated and measured? As a research domain that will undoubtedly become more complex and diverse in the coming decade, it is essential to generate a synthesized understanding of the governance systems in professional regulation about technological change and internationalization. Policymakers and regulators are currently exploring regulatory solutions in these areas, making this a governance priority. To address our research questions, we will undertake a scoping review. Scoping reviews are applicable when examining the extent, range, and nature of evidence in a given domain, especially when it is difficult to visualize the range of evidence that might be available. To operationally guide our knowledge synthesis, we will use the six-step scoping review framework articulated by Arksey and O’Malley and further described by Levac, Colquhoun, and O’Brien, as well as the corresponding guidance from the Joanna Briggs Institute Reviewer’s Manual.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,123 | 0,144 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,017 | 0,075 |
| Communication savante | 0,034 | 0,029 |
| Science ouverte | 0,003 | 0,018 |
| Intégrité de la recherche | 0,014 | 0,013 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».