Exploring the nature of facilitating interprofessional learning: findings from an exploratory study
Bibliographic record
Abstract
With the growing complexity in managing multiple disease and illness-related problems, increased attention is being paid to the importance of interprofessional education (IPE) in preparing students for working collaboratively with different professionals. Educational activities for mixed groups of health professional students are increasing, and facilitation of learning in interprofessional student groups is now acknowledged as an essential part of successful interprofessional learning activities. However, little is known about the strategies used by facilitators with students from different professions, and how they promote learning. Using data obtained through an analysis of videos taken as part of a large study of IPE and interprofessional practice, this study aimed to identify the pedagogical strategies and behaviours of facilitators participating in seven different learning activities with health care students from five different professions. The data captured student reactions and behaviours and provided insight into the dynamics of the interprofessional encounters. The findings showed that facilitating groups involved a complex interchange of three types of interaction between facilitators and students: facilitator-controlled interaction, facilitator-driven interaction and student-driven interactions. The findings also suggest that faculty development programs should assist facilitators to re-examine teaching approaches and encourage students to assume the responsibility for discussing issues and collaborating with others in all their interprofessional contacts. Continuity and stability in faculty development activities will better prepare clinical educators and young professionals to become interprofessional champions.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".