Key challenges for implementing a Canadian-based objective structured clinical examination (OSCE) in a Middle Eastern context
Bibliographic record
Abstract
Globalization of medical education is occurring at a rapid pace and many regions of the world are adapting curricula, teaching methods, and assessment tools from established programs. In the Middle East, the use of Objective Structured Clinical Examinations (OSCEs) is rare. The College of Pharmacy at Qatar University recently partnered with the University of Toronto and the Supreme Council of Health in Qatar to adapt policies and procedures of a Canadian-based OSCE as an exit-from-degree assessment for pharmacy students in Qatar. Despite many cultural and contextual barriers, the OSCE was implemented successfully and is now an integrated component of the pharmacy curriculum. This paper aims to provide insight into the adoption and implementation process by identifying four major cultural and contextual challenges associated with OSCEs: assessment tools, standardized actors, assessor calibration, and standard setting. Proposed solutions to the challenges are also given. Findings are relevant to international programs attempting to adapt OSCEs into their contexts, as well as Canadian programs facing increasing rates of cultural diversity within student and assessor populations.
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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 | 0.124 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".