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Record W1965823720 · doi:10.1002/chp.20069

Development and testing of a scale to assess interprofessional education (IPE) faciliation skills

2010· article· en· W1965823720 on OpenAlexaff
Joan Sargeant, Tanya Hill, Lynn M. Breau

Bibliographic record

VenueJournal of Continuing Education in the Health Professions · 2010
Typearticle
Languageen
FieldHealth Professions
TopicInterprofessional Education and Collaboration
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsFacilitatorInterprofessional educationFacilitationScale (ratio)PsychologyMedical educationInterpersonal communicationMedicineHealth careSocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Interprofessional education (IPE) is interactive and constructivist in nature and requires specific facilitation skills to engage participants in a unique body of content, interpersonal interaction, and learning from each other. This article describes the development and testing of a scale, the Interprofessional Facilitation Scale (IPFS), to assess educators' skills in facilitating IPE. METHODS: Following participation in an Interprofessional Facilitator Development Program, facilitators provided interprofessional workshops for health professionals caring for patients with cancer. Workshop participants (311 community health professionals) assessed IPE facilitation skills with the use of the IPFS. RESULTS: Psychometric testing of the scale demonstrated high reliability and strong construct and content validity. Factor analysis produced a 2-factor solution that explained 62.1% of the scale variance. The factors "Encouraging IP interaction" and "Contextualizing IPE" were psychometrically rigorous and supported by the literature as being critical to facilitating successful IPE. DISCUSSION: The IPFS can be used in facilitator development as a concise guide to IPE facilitation skills and for assessment and further enhancement of IP facilitation competencies. Further study is required to assess the scale in diverse settings, with preservice learners, and over the longer term.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.402
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.041
GPT teacher head0.471
Teacher spread0.430 · 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 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

Citations69
Published2010
Admission routes1
Has abstractyes

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