Interprofessional Education: A Systematic Review of Educational Methods in Postgraduate Health Professions Programs
Bibliographic record
Abstract
BACKGROUND: Several studies on interprofessional education (IPE) explored student's knowledge acquisition, teamwork skills and collaborative behaviours. However, the approaches to teaching and learning IPE remain underresearched and reported especially at the postgraduate level. This systematic review aimed to establish how IPE has been implemented at the postgraduate level among different health professions, focusing on teaching and learning approaches. METHODS: The systematic review was conducted in 2022-2025. It utilized three carefully identified databases: PubMed, ScienceDirect and the Cochrane Library. Publications were included after being screened based on a clear protocol and preidentified eligibility criteria. The research team used CADIMA software to screen articles published from 2010 to 2025. RESULTS: Thirty-seven articles were considered in this systematic review. These articles were mainly from the United States, United Kingdom and Canada. Various educational approaches and a wide variety of tools were utilized to deliver IPE among health professionals at the postgraduate level. Yet, the findings indicated that simulation was at the top of the used approaches. This systematic review also revealed that IPE activities at the postgraduate level need to be focused more on interprofessional role learning and dual identity development. CONCLUSIONS: The evidence synthesized in the current systematic review reveals that educators prefer simulation to deliver IPE activities. Yet, the evidence calls for more focused planning for IPE activities at the postgraduate level to advance IPE delivery and ensure targeting immersion and mastery levels of IPE development.
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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 | 0.026 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".