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Record W1990197734 · doi:10.3109/13561820.2014.1002907

Legislating interprofessional collaboration: A policy analysis of health professions regulatory legislation in Ontario, Canada

2015· article· en· W1990197734 on OpenAlexafffundabout
Sandra Regan, Carole Orchard, Hossein Khalili, Laura Brunton

Bibliographic record

VenueJournal of Interprofessional Care · 2015
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of TorontoFanshawe CollegeWestern University
FundersHealth CanadaWorld Health Organization
KeywordsLegislationLegislatureCLARITYGovernment (linguistics)Public relationsPublic administrationObligationPolitical scienceLaw

Abstract

fetched live from OpenAlex

Changes to Ontario's health professions regulatory system were initiated through various legislative amendments. These amendments introduced a legislative obligation for health regulatory colleges to support interprofessional collaboration (IPC), collaborate where they share controlled acts, and incorporate IPC into their quality assurance programs. The purpose of this policy analysis was to identify activities, strategies, and collaborations taking place within health professions regulatory colleges pertaining to legislative changes related to IPC. A qualitative content analysis of (1) college documents pertaining to IPC (n = 355) and (2) interviews with representatives from 14 colleges. Three themes were identified: ideal versus reality; barriers to the ideal; and legislating IPC. Commitment to the ideal of IPC was evident in college documents and interviews. Colleges expressed concern about the lack of clarity regarding the intent of legislation. In addition, barriers stemming from long-standing issues in practice including scope of practice protection, conflicting legislation, and lack of knowledge about the roles of other health professionals impede IPC. Government legislation and health professional regulation have important roles in supporting IPC; however, broader collaboration may be required to achieve policy objectives.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.423
Teacher spread0.384 · 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

Citations23
Published2015
Admission routes3
Has abstractyes

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