Exploring Preparedness, Motivation, Communication Tools and Strategies for the Engagement of Facilitators in Interprofessional Education
Bibliographic record
Abstract
Interprofessional education (IPE) case-based events aim to nurture effective communication and active engagement among students from diverse disciplines. Facilitators play a critical role in the success of these events, yet limited research explores how to best prepare and motivate facilitators to fully engage students. This study addresses this gap by examining structured facilitator preparedness, motivational factors, and communication tools that enhance student engagement. Seven facilitators with prior IPE experience participated in semi-structured interviews to share their perspectives on preparation, motivation, and effective communication strategies. The interviews, lasting 45 to 60 minutes, were guided by questions adapted from Dalhousie University's Interprofessional Facilitation Scale (IFS) and included standardized prompts for discussion. Transcriptions of the sessions were analyzed to identify actionable strategies for facilitator development. The results revealed that structured preparation materials, such as case study notes and facilitation guides, significantly enhanced facilitators’ confidence and readiness to lead sessions. Motivation was closely tied to facilitators’ commitment to creating interdisciplinary collaboration and improving student learning outcomes. Effective communication tools, including structured prompts and guided discussion frameworks, were found to support student engagement and meaningful group interactions.This study provides educational institutions with practical strategies for enhancing facilitator training and support, emphasizing the importance of tailored preparation, motivational reinforcement, and effective communication techniques. These findings contribute to the development of stronger interdisciplinary collaboration and enriched student learning experiences in IPE settings.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".