OSCEs’ Impact on Occupational Therapy Student Learning: Insights from Second- and Third-Year Focus Groups
Bibliographic record
Abstract
Background: Objective Structured Clinical Examinations (OSCEs) are widely used in health programs to assess clinical skills. We present results of a qualitative study investigating occupational therapy students’ perceptions of OSCEs’ impact on their learning and readiness for clinical practice. Method: Six second and six third year students in the University of Alberta’s Master of Science in Occupational Therapy program were interviewed in separate focus groups. Independent reviewers applied thematic analysis to the focus group transcripts to identify, analyze, and report themes in the data. Results: Five themes were constructed from the data: from learning to action, transition to practice, stress, representativeness, and suggestions for improvement. Both cohorts perceived OSCEs as intensely stressful but ultimately beneficial to their learning, though third-years more readily identified stress as a catalyst for personal and professional growth. Further, both cohorts noted that OSCEs motivated them to practice clinical skills and constituted important stepping stones toward authentic practice, but the third-year students more frequently drew connections between the skills tested in their OSCEs and their confidence in working as occupational therapists. Conclusion: OSCEs play an important role in forming students’ identities as clinicians in the making, supporting their continued use for formative assessment in MScOT programs.
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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 | 0.022 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".