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Record W2028762752 · doi:10.1080/13561820500082354

Models of interprofessional learning in Canada

2005· article· en· W2028762752 on OpenAlexafffundabout
David Cook

Bibliographic record

VenueJournal of Interprofessional Care · 2005
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Alberta
FundersHealth CanadaCollege of Family Physicians of Canada
KeywordsInterprofessional educationScope (computer science)Health professionsMedical educationScope of practiceFunction (biology)Work (physics)PsychologyMedicineHealth careComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This article provides an overview of interprofessional education in Canada, with a view to defining programs at all levels in terms of what models have been employed. The available information implies that the lack of convincing evidence of the effectiveness of existing programs is probably the most serious problem for the expansion of interprofessional education. The objectives of the programs are both to increase the knowledge about the other professions and their scope of practice, and to improve team function, and there are a number of well-established interprofessional programs in Canada that are designed to achieve these objectives, and many other examples of programs that are partial or planned. Despite this, the present interprofessional education initiatives tend to involve only a small proportion of the total health work trainees. There is a need for programs that are more widespread. The most frequent model involves a mandatory experience, which is case-based, involves all the students registered in Health Faculties, and where the students form interprofessional student teams. In addition to examining believable cases, the students also learn some specific information about interacting with the other professions and gain knowledge about the roles, knowledge and contributions that can be made by professions other than their own.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.399
Teacher spread0.378 · 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

Citations75
Published2005
Admission routes3
Has abstractyes

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