Insights from healthcare academics on facilitating interprofessional education activities
Bibliographic record
Abstract
Facilitators are of paramount importance to the success of interprofessional education (IPE) activities; hence, it is crucial to explore their perspectives and experiences in delivering IPE in Qatar. Using an exploratory case study approach, semi-structured interviews were conducted, in 2018, among faculty members, who had facilitated at least one IPE activity in Qatar, from healthcare professional education programs at Qatar University Colleges of Pharmacy, Medicine, and Health Sciences, Weill Cornell Medicine in Qatar, the University of Calgary in Qatar, and the College of North Atlantic. Interviews were recorded and transcribed verbatim. Inductive thematic content analysis was implemented. Twenty-one interviews were conducted with the following professions represented: medicine (n = 6), pharmacy (n = 5), nursing (n = 4), biomedical science (n = 3), respiratory theory (n = 2) and public health (n = 1). Four main themes emerged from the interviews: drivers to facilitator involvement that included interest and commitment to IPE and awareness of collaborative practice benefits; facilitator participation which was based on facilitator attributes and preparedness and readiness for IPE facilitation; the organizational support in terms of dedicated structure for IPE and IPE design and delivery and; student participation in terms of group dynamics and student engagement. Some key recommendations include having a dedicated unit for IPE, scheduling protected time for IPE, and organizing facilitators' training and debriefing workshops. The facilitators valued and appreciated IPE in preparing students for future collaborative practice. These findings can inform the development of quality and sustainable IPE activities in the future.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".