Comparative Effectiveness of Training with Simulators Versus Traditional Instruction in Veterinary Education: Meta-Analysis and Systematic Review
Bibliographic record
Abstract
One of the greatest challenges in veterinary education is adequately preparing students with the clinical skills they need to be successful health care providers. Integration of simulators, devices used to represent real world phenomena under test conditions, into the veterinary curriculum can help address challenges to clinical instruction. The use of simulators in veterinary education is increasing; however, their effectiveness remains unclear. This meta-analysis seeks to synthesize the evidence for simulator training in veterinary education to provide a consensus of effect, guide the integration of simulators into the curriculum, and direct the development of future simulation-based research in veterinary medicine. A systematic search identified 416 potential manuscripts from which 60 articles were included after application of inclusion criteria. Information was extracted from 71 independent experiments. The overall weighted mean effect size for simulator training was g = 0.49 for the random-effects model. The outcome measures of knowledge, time, process, and product all produced statistically significant mean effect sizes favoring simulation ( d = 0.41, 0.35, 0.70, 0.53, respectively). A moderator analysis revealed that study characteristics and instructional design features moderated the effectiveness of simulator training. Overall, the results indicate that simulator training in veterinary education can be effective for knowledge and clinical skill outcomes.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".