Investigating the Effects of Error Management Training versus Error Avoidance Training on the Performance of Veterinary Students Learning Blood Smear Analysis
Bibliographic record
Abstract
Conventional veterinary training emphasizes correct methodologies, potentially failing to exploit learning opportunities that arise as a result of errors. Error management training (EMT) encourages mistakes during low-stakes training, with the intention of modifying perceptions toward errors and using them to improve performance in unfamiliar scenarios (adaptive transfer). Herein, we aimed to determine the efficacy of EMT, supplemented by a metacognitive module, for veterinary students learning blood smear preparation and interpretation. Our hypothesis was that EMT and metacognition are associated with improved adaptive transfer performance, as compared with error avoidance training (EAT). A total of 26 students were prospectively enrolled in this double-blind study. Performance was evaluated according to monolayer area, smear quality, cell identification, calculated white blood cell differential counts, and overall application/interpretation. Students were trained with normal canine blood and static photomicrographs. Participants tested 72 hours after training demonstrated improved performance in a test that directly recapitulated training (Wilcoxon matched-pairs signed-rank test; two-tailed p all ≤ .001). There were no significant differences between EAT and EMT in this test (Mann–Whitney U test and Welch’s t-test; two-tailed p ≥ .26) or in short- and long-term adaptive transfer tests ( p ≥ .22). Survey data indicate that participants found errors to be a valuable element of training, and that many felt capable of accurately reflecting on their own performance. These data suggest that EMT might produce outcomes comparable to EAT as it relates to blood smear analysis.
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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 | 0.011 |
| 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".