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Record W128938709

An interprofessional practice capability framework focusing on safe, high-quality, client-centred health service.

2013· article· en· W128938709 on OpenAlexaboutno aff
Margo Brewer, Sue Jones

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsnot available
Fundersnot available
KeywordsInterprofessional educationCurriculumMedical educationQuality (philosophy)Health careNursingMedicineKnowledge managementPsychologyComputer sciencePedagogyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper describes an interprofessional capability framework which builds on the existing interprofessional competency and capability frameworks from the United Kingdom, Canada, and the United States of America. Existing published frameworks generally make reference to being client-centred and to the safety and quality of care, and locate interprofessional collaborative practice as the central theme or objective. In contrast, this framework interlinks all three elements: client-centred services, safety and quality of services, and interprofessional collaborative practice. The framework is clear and succinct with an accompanying visual representation that highlights all key features. The framework has informed curriculum which incorporates a common first-year, case-based educational workshops and practice placements within a large complex health sciences faculty of approximately 10,000 students from 22 disciplines. The articulation of these key elements of health practice has facilitated students, academic staff, and community health professionals to develop a shared understanding of interprofessional education and practice. The design, implementation, and evaluation of learning outcomes, learning experiences, and assessments have been transformed with the introduction of this framework, which is highly applicable to other contexts.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.848
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.071
GPT teacher head0.452
Teacher spread0.382 · 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; both teacher heads agree on what is shown here.

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

Citations60
Published2013
Admission routes1
Has abstractyes

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