Successful implementation of interprofessional education: A pedagogical design perspective
Bibliographic record
Abstract
Interprofessional collaboration (IPC) is crucial within healthcare teams that must provide safe and quality care to their patients. Competent professionals in this area offer better care and contribute to a medical culture where IPC and teamwork are valued. To become competent, they must be adequately trained. The literature describes that, unfortunately, collaboration training is uneven across professions. Interprofessional education (IPE) could fill this educational gap but remains challenging to implement. This article aims to present ten clear and concise considerations to implementing IPE initiatives successfully, following a well-described pedagogical designing process. After reading, the clinician-educator will be informed of the newest evidence in IPE as well as the common pitfalls to avoid. From the starting point of a recent synthesis article on IPE, several additional syntheses, analyses, and recommendations articles were consulted and synthesized. From that, the findings are organized according to the "ADDIE" model, a flexible methodology used in pedagogical design through iterative cycles in context. The phases of "ADDIE" are analysis, design, development, implementation, and evaluation. According to these phases, the considerations will be presented to allow the reader to apply them "step by step" in their educational planning process. Ten considerations are presented, from the needs analysis, stakeholders and Faculty involvement, composition of the design team, selection of students and types of learning activity, the role of reflexivity, training of facilitators, supervision, and the continuous improvement process. Taken together, these will contribute to highlighting the essential nature of training in collaboration in modern professionalizing programs.
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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 | 0.051 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| 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".