Challenges & Issues: Evidence-Based Clinical Skills Teaching and Learning: What Do We Really Know?
Bibliographic record
Abstract
The recent programmatic focus on skills development in veterinary medicine means that many programs are devoting increased time to formal clinical skills teaching. This expansion makes it essential that we use the time as effectively as possible. This review examines current practices and veterinary training principles using the broader field of evidence-based motor skills learning as a lens. In many areas, current practices may be hindering learning. Proposed practices include using videos and discussions for pre-laboratory training, focusing on a single complex skill at a time, using more near-peer instructors rather than faculty, including assessments in each teaching or practice session, and encouraging supervised distributed practice by incorporating practice sessions into the formal curriculum. Ensuring mastery of a few core skills rather than exposure to many may be the new goal. Further research is urgently needed on block versus spiral curricula, optimum instructor-to-student ratios, learning and practice schedules, hours required for proficiency, and the benefits of exercise on motor skills learning.
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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 | 0.092 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".