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Record W2153320792 · doi:10.3109/13561820902921654

Performance-based competencies for culturally responsive interprofessional collaborative practice

2009· article· en· W2153320792 on OpenAlexaff
Valerie Banfield, Kelly Lackie

Bibliographic record

VenueJournal of Interprofessional Care · 2009
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsRegistered Nurses' Association of OntarioNova Scotia Health Authority
Fundersnot available
KeywordsFacilitatorInterprofessional educationMedical educationCurriculumVariety (cybernetics)MedicineCertificationHealth careNursingKnowledge managementPsychologyPedagogyComputer science

Abstract

fetched live from OpenAlex

This paper will highlight how a literature review and stakeholder-expert feedback guided the creation of an interprofessional facilitator-collaborator competency tool, which was then used to design an interprofessional facilitator development program for the Partners for Interprofessional Cancer Education (PICE) Project. Cancer Care Nova Scotia (CCNS), one of the PICE Project partners, uses an Interprofessional Core Curriculum (ICC) to provide continuing education workshops to community-based practitioners, who as a portion of their practice, care for patients experiencing cancer. In order to deliver this curriculum, health professionals from a variety of disciplines required education that would enable them to become culturally sensitive interprofessional educators in promoting collaborative patient-centred practice. The Registered Nurses Professional Development Centre (RN-PDC), another PICE Project partner, has expertise in performance-based certification program design and utilizes a competency-based methodology in its education framework. This framework and methodology was used to develop the necessary interprofessional facilitator competencies that incorporate the knowledge, skills, and attitudes required for performance. Three main competency areas evolved, each with its own set of competencies, performance criteria and behavioural indicators.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.437
Teacher spread0.415 · 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 designQualitative
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

Citations38
Published2009
Admission routes1
Has abstractyes

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