Enhancing the Facilitation of Interprofessional Education Programs: An Institutional Ethnography
Bibliographic record
Abstract
Interprofessional collaboration (IPC) among health care professionals has been identified as essential to enhance patient care. Interprofessional education (IPE) is a key strategy towards promoting IPC. Several factors including the nature of facilitation shape the IPE experience and outcomes for students. Stereotypes held by students have been recognized as a challenge for IPE and IPC. This study aimed to explore institutional rules and regulations that shape facilitators' work in IPE interactions problematized by students' stereotypes at a university in Atlantic Canada. Employing institutional ethnography as a method of investigation, data were collected through observations, interviews, focus groups, and written texts (such as course syllabi). Participants included three facilitators, two undergraduate nursing students, and two IPE committee members of an IPE program. Findings revealed four work processes conducted by facilitators in local IPE settings related to students' stereotypes. These processes were shaped by translocal discourse and included the work used to form teams, facilitate student introductions to team members, facilitate team dynamics, and provide course content and context. Study results included the identification of several strategies to address student stereotypes and enhance collaboration, including directions for future curriculum decisions and the pedagogical organization of IPE.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".