Comparison of Self-Directed and Instructor-Led Practice Sessions for Teaching Clinical Skills in Food Animal Reproductive Medicine
Bibliographic record
Abstract
While the use of simulator-based clinical skill training has become increasingly popular in veterinary education in recent years, little research has been done regarding optimal implementation of such tools to maximize student learning in veterinary curricula. The objective of this study was to compare the effects of supervised and unsupervised deliberate practice on clinical skills development in veterinary medicine students. A total of 150 veterinary students took part in instructor-led practice (supervised) or self-directed practice (unsupervised) at a selection of four learning stations in a veterinary skills laboratory. Each learning station consisted of a teaching simulator, materials required to complete the task, and a standard operating procedure detailing how to execute the task. Students used Likert scales to self-evaluate their clinical skills before and after practice sessions, in addition to evaluating their motivation to practice a given task. An objective structured clinical examination (OSCE) was used to compare participants' clinical skills performance between learning stations. We were able to show that practice had a significant positive effect on OSCE scores at three out of six available learning stations. Motivation ratings varied between learning stations and were positively correlated with an increase in self-perceived clinical skills. At an instructor-to-student ratio of approximately 1:8, supervision had no effect on OSCE scores at four out of six learning stations. At the remaining two learning stations, self-directed practice resulted in better learning outcomes than instructor-led practice.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".