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Record W3124360802 · doi:10.4324/9781315586366-19

Interprofessionalism and Collaborative Self- regulation in the Health Professions: two Variations on an Emerging Canadian Theme

2016· book-chapter· en· W3124360802 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsTheme (computing)Political sciencePsychologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Interprofessional collaboration among regulated health care professionals has received significant attention in Canadian health policy literature (Barrett 2005, Bosco 2005, Canadian Health Services Research Foundation 2006, Côté et al. 2008, Health Council of Canada 2009). This reflects widespread confidence that interprofessional collaborative care, or team-based care, will contribute to a wide range of interrelated and escalating benefits for patients, providers and the broader health care system. Notwithstanding the doubts of some (Cameron 2011, Greenfield et al. 2011), these benefits are thought to include expanded access (and choice of access) to necessary services; enhanced clinical quality and safety; reduced burdens on the members of some professions and expanded opportunities for the members of others; and a health care system that becomes more responsive to patient needs while simultaneously becoming more sustainable by making better, fuller and more flexible use of the competencies of all providers.

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)0.002
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.397
Threshold uncertainty score0.799

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0070.022
Scholarly communication0.0090.006
Open science0.0010.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.040
GPT teacher head0.434
Teacher spread0.394 · 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
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

Citations4
Published2016
Admission routes1
Has abstractyes

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