Action, Reflection, and Evolution: A Pilot Implementation of Interprofessional Education across Three Rehabilitation Disciplines
Bibliographic record
Abstract
Background: Interprofessional collaboration (IPC) is accepted as standard practice in healthcare. Because of this expectation, there is an increased need for growth in interprofessional education (IPE). Despite this need, the scholarship of IPE is limited. To better understand the challenges of IPE and improve on future endeavours, this article describes an IPE collaboration that was less successful, and the conclusions drawn from team reflection regarding IPE. We report on the challenges and the lessons learned.Methods and Findings: After one year of an IPE pilot project, the research team conducted a reflection exercise involving three iterations: 1) initial group meeting to discuss reflection questions, 2) individual review of meeting notes, 3) subsequent group meeting to confirm accuracy of the data. The confirmed data were then analyzed using thematic analysis.Conclusions: The key themes that emerged regarding the limited success of the pilot were focused on communication—between members of the research team, with the students, and with other faculty impacted by the pilot. Recommendations regarding improvements to facilitate future IPE initiatives are discussed. The summary conclusion of our exercise acknowledged that as IP educators we must remain vigilant to demonstrate IPC in the same manner as we teach it.
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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 | 0.041 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".