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Record W2319635965 · doi:10.3109/13561820.2016.1139557

Enhancing interprofessional education and practice: Development and implementation of a new graduate-level course using the international classification of functioning, disability, and health

2016· article· en· W2319635965 on OpenAlexaff
Tram Nguyen, Nora Fayed, Jan Willem Gorter, Joy C. MacDermid

Bibliographic record

VenueJournal of Interprofessional Care · 2016
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthBiopsychosocial modelMentorshipMedical educationHealth carePsychologyMedicineNursingRehabilitationPhysical therapy

Abstract

fetched live from OpenAlex

Since the introduction of the World Health Organization's International Classification of Functioning, Disability and Health (ICF), there is increasing awareness among health professionals to consider a biopsychosocial approach to patients' health and functioning. Although diffusion of the ICF as a concept is widely recognized, application of the ICF within health education and practice requires attention, training, and support. This article describes the development and implementation of a new graduate-level course using the ICF to assist health professionals and graduate trainees in rehabilitation. The innovation behind this course is its focus on application of the ICF in research and practice through a combination of peer support and instructor mentorship. The value of the ICF for interprofessional education, research, and practice includes promotion of a broad perspective to health, application of theory in practice, and enhanced communication and collaboration in healthcare.

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)0.006
metaresearch head score (Gemma)0.009
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.145
GPT teacher head0.537
Teacher spread0.392 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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