Using an OSCE to Explore the Role of Structured Debriefing and Self-Directed Learning in Simulator-Based Clinical Skill Training in Production Animal Reproductive Medicine
Bibliographic record
Abstract
Self-directed learning is associated with several benefits in simulation-based clinical skill training and can be complemented by feedback in the form of post-event debriefing. In this study, final-year veterinary medicine students ( n = 111) were allocated into one of three groups and practiced four clinical skills from the domain of production animal reproductive medicine in a clinical skills laboratory. Group 1 completed an instructor-led practice session (I), group 2 completed a self-directed practice session with post-event debriefing (D), and group 3 completed a self-directed practice session without debriefing (control, C). Each practice session included two clinical skills categorized as being directly patient-related ( patient) and two clinical skills involving laboratory diagnostics or assembling equipment ( technical). Students evaluated the practice session using Likert-type scales. Two days after practice, 93 students took part in an objective structured clinical examination (OSCE). Student performance was analyzed for each learning station individually. The percentage of students who passed the OSCE did not differ significantly between the three groups at any learning station. While the examiner had an effect on absolute OSCE scores (%) at one learning station, the percentage of students who passed the OSCE did not differ between examiners. Patient learning stations were more popular with students than technical learning stations, and the percentage of students who passed the OSCE was significantly larger among students who enjoyed practicing at the respective station (90.9%) than among those who did not (77.8%). This translation was provided by the authors. To view the full translated article visit: https://doi.org/10.3138/jvme-2021-0060.de
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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 | 0.025 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".