Confidence, Competence and Cadavers: Improving the Self-Perception of Practice through Practical Teaching
Bibliographic record
Abstract
Practical sessions using cadavers are one method of teaching clinical skills en masse to veterinary students, supporting students to learn the skills and gain the confidence to become Day One competent veterinarians. As confidence and competence are often conflated in Competency-Based Education approaches, in this study we used a pre-post survey design to evaluate 67 student self-ratings of confidence and self-assessed competence to explore whether a cadaver practical can change student confidence and self-assessed competence, and how comparable confidence and self-assessed competence are as measures. In a linear mixed effects model, we found that the practical improved the overall confidence score by 0.44 points (95% confidence interval [CI] [0.22, 0.66], t(120) = 3.97, p < .001). Self-assessed competence also increased by 0.60 (95% CI [0.41, 0.79], t(118) = 6.21, p < .001). However, although female students saw their overall self-assessed competence increase, they showed lower self-assessed competence scores by 0.87 points than males (95% CI [−1.47, −0.28], t(118) = −2.89, p = 0.005). Despite confidence and self-assessed competence being strongly associated, direct agreement between the measures in a weighted kappa test was weak (pre-practical κweighted = 0.49, [95% CI 0.33, 0.66], post-practical κweighted = 0.44, [95% CI 0.10, 0.28]). We discuss the implications of these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".