The implementation of interprofessional education: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Implementation of interprofessional education (IPE) is recognised as challenging, and well-designed programs can have differing levels of success depending on implementation quality. The aim of this review was to summarise the evidence for implementation of IPE, and identify challenges and key lessons to guide faculty in IPE implementation. METHODS: Five stage scoping review of methodological characteristics, implementation components, challenges and key lessons in primary studies in IPE. Thematic analysis using a framework of micro (teaching), meso (institutional), and macro (systemic) level education factors was used to synthesise challenges and key lessons. RESULTS: Twenty-seven primary studies were included in this review. Studies were predominantly descriptive in design and implementation components inconsistently reported. IPE was mostly integrated into curricula, optional, involved group learning, and used combinations of interactive and didactic approaches. Micro level implementation factors (socialisation issues, learning context, and faculty development), meso level implementation factors (leadership and resources, administrative processes), and macro level implementation factors (education system, government policies, social and cultural values) were extrapolated. Sustainability was identified as an additional factor in IPE implementation. CONCLUSION: Lack of complete detailed reporting limits evidence of IPE implementation, however, this review highlighted challenges and yielded key lessons to guide faculty in the implementation 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 | 0.027 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".