Investigating the Effects of Error Management Training versus Error Avoidance Training on the Performance of Veterinary Students Learning to Tie Surgical Knots
Bibliographic record
Abstract
Although errors can be a powerful impetus for learning, conventional pedagogy often emphasizes error-avoidance strategies that reward correct answers and disfavor mistakes. Error management training (EMT) takes an explicitly positive approach to errors, using them to create an active and self-directed learning environment. Using a surgical knot–tying model, we aimed to determine the efficacy of EMT among veterinary students with no prior surgical experience. We hypothesized that EMT would result in improved performance in unfamiliar scenarios (adaptive transfer) compared with an error-avoidance method. In this prospective double-blinded study, 42 students were equally divided between error avoidance training (EAT) and EMT groups. Performance in instrument- and hand-tied knots was evaluated for technique, time, number of attempts, and, when applicable, knot-leaking pressure. All participants demonstrated significant improvement between a pre-test and an analogous test 48 hours after training for all six outcomes (Wilcoxon matched pairs; two-tailed ps ≤ .013). An adaptive transfer test found no significant differences between EMT and EAT at 48 hours ( ps ≥ .053). All participants demonstrated a significant performance decline in six of eight outcomes at 7 weeks post-training ( ps ≤ .021). This decline was not significant for four of six EMT outcomes yet significant for five of six EAT outcomes. These data suggest that students trained in both EMT and EAT experience comparable gains in short-term performance, including adaptive transfer. Compared with EAT, EMT may help attenuate performance decline after a sustained period of quiescence. Educators may consider actively incorporating EMT into veterinary curricula.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".