Impact of Clinical Skills Laboratory Training and Online Education on Suture Skill Development in Veterinary Students: A Gender-Based Analysis
Bibliographic record
Abstract
The acquisition of clinical skills is a fundamental component of veterinary education, necessitating effective instructional methods that balance theoretical knowledge and practical application. Although this study primarily aimed to assess the effectiveness of clinical skills laboratory (CSL) training in skill development of first-year veterinary students, an emerging observation was the gender-based differences in skills acquisition and improvement. Given the limited existing research on this aspect, these findings contribute to the understanding of potential gender-related learning variations in surgical training. In this prospective, blinded, randomized clinical trial, 140 first-year veterinary students were tasked with basic suturing exercises. Performance scores demonstrated improvement across all assessed skills, with notable gains in suturing proficiency following CSL training. Students who participated in hands-on practice achieved significantly higher post-test scores compared with those who relied solely on online instruction, reinforcing the effectiveness of practical training. Notably, female students in both groups exhibited a statistically higher increase in performance scores than their male counterparts. These findings underscore the importance of practical, model-based training in CSL for fostering skills acquisition and revealed the impact of gender on skill development. This study contributes to the growing body of evidence supporting the integration of experiential learning into veterinary education and offers insights into optimizing training methods to enhance student 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".