Examining the Role of Structured Debriefing in Simulator-Based Clinical Skills Training for Namibian Veterinary Students: A Pilot Study
Bibliographic record
Abstract
Post-event debriefing has been described as an effective tool in improving learning achievements in simulator-based teaching. This article examines the effect of structured post-event debriefing sessions in simulator-based veterinary clinical skills training. Nineteen Namibian veterinary students took part in instructor-led practice, self-directed practice with structured post-event debriefing and self-directed practice without debriefing (control) at three different learning stations in a veterinary clinical skills laboratory. Students evaluated their practice experience using Likert-type scales, and learning achievements were assessed using an objective structured clinical examination (OSCE). The results show that the choice of practice model had no significant effect on learning achievements overall. However, at individual learning stations, different practice models showed significant differences regarding effect on learning achievements. Students generally preferred practice sessions with some form of instructor involvement but the importance of instructor guidance was rated differently at each individual learning station.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".