IMPLEMENTATION OF AN OBJECTIVE STRUCTURED CLINICAL EXAMINATION (OSCE) IN A KINESIOLOGY BACHELOR DEGREE
Bibliographic record
Abstract
"For the past four years, the Laval University’s kinesiology bachelor degree has been using an OSCE to assess their students’ clinical competencies. This presentation will describe the format the OSCE and will discuss the quality improvement process implemented following psychometric analysis of the test and its nine stations. The psychometrics qualities were tested using the Cronbach’s alpha, the stations’ difficulty level and discrimination index. An ANOVA has also been realised to ensure that the students’ results of a same station in a different circuit were equivalent regarding the examiners. In the 2018 edition, the psychometrics qualities were under the standards, especially the Cronbach’s alpha and the stations’ discrimination index. In order to improve the reliability of the test, modifications were made to seven of nine scoring grids. A committee revised each competencies’ component assessed and removed the ambiguous ones. The psychometrics qualities of the revised results improved accordingly. To prepare the 2019 edition, the OSCE committee reviewed the nine stations and adjusted the scoring grids. It also designed three new clinical situations. The psychometrics qualities of the 2019 edition have shown an improvement of the Cronbach’s alpha and the stations’ discrimination index. It has also demonstrated no significant differences between the circuits’ performance. The appreciation surveys administered following each edition revealed the quality of the support offered to the students, examiners and simulated patients and the authenticity of the clinical situations. We thus consider this OSCE to be a reliable method to assess students’ competencies of our kinesiology program."
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".