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Record W2132048067 · doi:10.1080/13561820500083188

Regulatory and medico-legal barriers to interprofessional practice

2005· article· en· W2132048067 on OpenAlexaffabout
William Lahey, Robert J. Currie

Bibliographic record

VenueJournal of Interprofessional Care · 2005
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInterprofessional educationNursingMedicinePolitical scienceHealth careMedical educationPsychologyLaw

Abstract

fetched live from OpenAlex

Unlike the other contributions to this issue, this paper is concerned with the prospects and potential ramifications of implementing interprofessional practice from the legal standpoint. The authors focus on the two forums where the major legal issues are likely to be played out: the laws under which health care professionals are regulated; and the law of professional malpractice as applied by the courts under the tort of negligence. The goal is to examine the regulatory and medico-legal barriers that might prevent or inhibit health care professionals from working together on an interprofessional basis, and to forecast the kinds of changes within legal systems which will be necessary to accommodate the change. The first part of the paper focuses on the legal regimes which govern the Canadian health care system, and argues that the essential integrity of the system of professional self-regulation must be protected in programs of reform that seek to create space for interprofessional practice. The authors also propose a number of specific initiatives of review and legislative change as examples of the role that legal reform can play in the shift to a culture of interprofessional regulation. The second part of the paper focuses on malpractice law and suggests that, while in the long term the superior quality of care brought about by interprofessional practice should produce less liability, in the short term interprofessional practice may fit uneasily within the legal constructs traditionally employed by the courts to evaluate malpractice claims. The authors propose three strategies designed to minimize this risk.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.318
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.429
Teacher spread0.418 · 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 designNot applicable
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

Citations64
Published2005
Admission routes2
Has abstractyes

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