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Record W406936387

Interprofessional education in Canada

2010· article· en· W406936387 on OpenAlexaffabout
Scott Reeves, Simon Kitto

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)Interprofessional educationHealth carePublic relationsSocial workPrincipal (computer security)MedicineNursingMedical educationPsychologyPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Purpose : The paper draws on findings from a synthesis of materials from an environmental scan and literature review that aimed to identify the key elements related to the development of interprofessional education (IPE) in Canada. As well as reporting main findings from this work, the paper also offers some ideas for future directions of IPE in this context. Methods : An extensive search was undertaken to identify materials (published and unpublished) which would inform an understanding of the elements linked to the development of IPE in Canada. Results : Over 300 materials (papers, reports, reviews, books, chapters) were identified and synthesized. Four key areas emerged: ‘Learning approaches, activities and methods’; ‘Facilitation elements’; ‘Planning elements’ and ‘Empirical elements’. Collectively, these results aim to provide an insight into the key elements related to the development of IPE in Canada over the past ten years. Principal Conclusion : While IPE in Canada is a relatively recent phenomenon, a number of significant developments have occurred in relation to learning, facilitation, planning and empirical activities.

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)0.005
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.915
Threshold uncertainty score0.619

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0090.027
Science and technology studies0.0070.002
Scholarly communication0.0050.002
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.001

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 head0.012
GPT teacher head0.378
Teacher spread0.366 · 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 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

Citations0
Published2010
Admission routes2
Has abstractyes

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