Challenges and Opportunities of Interprofessional Education in Occupational Health and Safety Engineering: Global Experiences and Localization Strategies for Iran
Bibliographic record
Abstract
This letter to the editor underscores the critical role of Interprofessional Education (IPE) in enhancing collaborative practice within Occupational Health and Safety (OHS) and HSE fields. Drawing on global evidence, IPE fosters mutual respect, clarifies professional roles (e.g., industrial hygienists, occupational nurses, safety engineers), and improves problem-solving capabilities in complex workplace settings . Successful models from Canada and Europe integrate joint curricula using simulations and problem-based learning, which strengthen soft skills like conflict resolution and teamwork . However, implementing IPE in Iran faces significant barriers, including: Siloed academic structures and rigid curricula, Insufficient technological infrastructure for collaborative learning, Cultural resistance among faculty accustomed to traditional methods, and Scarcity of localized educational resources. To address these challenges, localization strategies emphasize: Curriculum reform to embed interdisciplinary projects, Faculty training in IPE methodologies, Investment in digital platforms (e.g., Virtual IPE/VIPE) , University-industry partnerships for practical training, and Culturally adapted content reflecting Iran’s socio-professional context . Quantitative evidence on IPE’s impact remains limited, but qualitative data indicate improved student confidence and team efficacy . For Iran, aligning these strategies with policy reforms and infrastructure development is vital to harness IPE’s potential in training a versatile OHS workforce.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| 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".