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Record W79910944

Core competencies for health care professionals: what medicine, nursing, occupational therapy, and physiotherapy share.

2006· article· en· W79910944 on OpenAlexaffabout
Sarita Verma, Margo Paterson, Jennifer Medves

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOccupational therapyCurriculumCore competencyMedicineMedical educationInterprofessional educationNursingHealth careRehabilitationNurse educationPhysical therapy educationHarmonizationPsychologyPhysical therapyPedagogyAccreditation
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the amalgamation of the core competencies identified for medicine, nursing, physical therapy, and occupational therapy and the "harmonization" of these competencies into a framework for interprofessional education. The study was undertaken at a Canadian university with a Faculty of Health Sciences comprised of three schools (namely, medicine, nursing, and rehabilitation therapy). Leaders in interprofessional education began to identify the common standards for the core competencies expected of learners in all three schools at commensurate levels to facilitate the integration of educational curricula aimed at interprofessional education across the Faculty. The model that was created serves as a basis for curriculum design and assessment of individuals and groups of learners from different domains across and within the four professions. It particularly highlights the relevance of cross-disciplinary competency teaching and 360-degree evaluation in teams. Most importantly, it provides a launch pad for clarifying performance standards and expectations in interdisciplinary learning.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.779
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

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

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 head0.127
GPT teacher head0.492
Teacher spread0.365 · 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.

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

Citations147
Published2006
Admission routes2
Has abstractyes

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