Development and testing of a scale to assess interprofessional education (IPE) faciliation skills
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".