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Regulation of health professions in Ontario: self-regulation with statutory- based public accountability

2019· article· en· W2954646923 on OpenAlexaffabout
Trudo Lemmens, Kanksha Mahadevia Ghimire

Bibliographic record

VenueRevista de Direito Sanitário · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccountabilityStatutory lawPublic relationsGovernment (linguistics)MisconductAppealPolitical sciencePublic administrationLaw

Abstract

fetched live from OpenAlex

The paper explores the model of regulation of health professionals in Ontario, Canada; a self-regulation model built around a detailed statutory scheme. The core of the paper consists of a discussion of Ontario’s Regulated Health Professions Act and of the key components of 26 specific health profession acts that have been enacted under its umbrella. The paper explores the role of the regulatory colleges, the role of the Ministry of Health in determining scope of practice and other components of medical practice, and the disciplinary and appeal procedures. Some other specific issues are also briefly touched upon, such as the integration into the profession of internationally trained physicians, and the government’s role in ensuring access to specialists across the province. A final section looks at the challenges and the limitations of the Ontario model, through a number of health professions-related controversies that reveal gaps in self-regulation, including: failure to set and enforce proper educational and practice standards in specific areas; failure to conduct timely investigations into potential misconduct by professionals; and failure to question professionals in a position of power. The paper also discusses briefly the implications of recognizing through legal regulation some alternative and complementary medical practices, and the challenge of regulating indigenous health care practitioners. It concludes that the primary limitations of the regulatory model arise on account of professional self-interest and power-relations impacting procedural issues, and the complexity of the regulatory model that may potentially undermine quality control.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.059
GPT teacher head0.388
Teacher spread0.329 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2019
Admission routes2
Has abstractyes

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