Objective Structured Clinical Examinations practices across Canadian medical schools: a national overview
Bibliographic record
Abstract
Introduction: Objective Structured Clinical Examinations (OSCEs) are crucial in assessing clinical competencies, but their implementation varies widely across medical schools. This work examines OSCE practices across Canadian medical schools, focusing on frequency, type, and timing. Methods: A survey was conducted among all 17 Canadian medical schools through the AFMC Clinical Skills Working Group. Data were collected during the 2023-2024 academic year. Details on OSCEs implementation during pre-clerkship and clerkship phases, categorized as formative or summative, and on the timing of final OSCEs was collected. Descriptive statistics were used to analyze the data. Results: The median number of OSCEs per school was four, with one-third formative and two-thirds summative. Pre-clerkship assessments were split between formative and summative OSCEs, while 78% of clerkship OSCEs were summative. Timing of a program's final OSCE varied: 35% occurred before the last year, while 65% took place in the final year, predominantly in the second half. All final OSCEs were summative. Discussion and Conclusion: Variability in OSCE implementation likely reflects differing curricular approaches and institutional constraints. This work demonstrated a more balanced proportion of formative and summative assessments during pre-clerkship, indicating a desire to provide students with opportunities learn from feedback during their early training years. During clerkship, the focus shifted towards summative assessments. The later emphasis on summative OSCEs may highlight a focus on certifying competence at the cost of reduced opportunities for formative feedback. Medical schools may use these findings as guidance when building their programs of assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".