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Record W2119953487 · doi:10.1177/0969733012468466

The ethics of interprofessional collaboration

2013· article· en· W2119953487 on OpenAlexaff
Joyce M. Engel, Dawn Prentice

Bibliographic record

VenueNursing Ethics · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsBrock University
Fundersnot available
KeywordsInterprofessional educationMainstreamHealth careNursingCurriculumPsychologyMedicineMedical educationPolitical sciencePedagogy

Abstract

fetched live from OpenAlex

Interprofessional collaboration has become accepted as an important component in today's health care and has been guided by concerns with patient safety, quality health-care outcomes, and economics. It is widely accepted that interprofessional collaboration improves patient outcomes through enhanced communication among health-care providers and increased accessibility to services. Although there is a paucity of research that provides confirmatory evidence, interprofessional competencies continue to be incorporated into the curricula of health-care students. This article examines the ethics of interprofessional collaboration and ethical issues that arise from the mainstream adoption of interprofessional competencies and the potential for moral distress in nursing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.056
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.294

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0560.066
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.061
Scholarly communication0.0090.006
Open science0.0010.010
Research integrity0.0070.009
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 head0.137
GPT teacher head0.552
Teacher spread0.416 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations98
Published2013
Admission routes1
Has abstractyes

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