Technical Skills Training for Veterinary Students: A Comparison of Simulators and Video for Teaching Standardized Cardiac Dissection
Bibliographic record
Abstract
The goal of the study was to evaluate alternative student-centered approaches that could replace autopsy sessions and live demonstration and to explore refinements in assessment procedures for standardized cardiac dissection. Simulators and videos were identified as feasible, economical, student-centered teaching methods for technical skills training in medical contexts, and a direct comparison was undertaken. A low-fidelity anatomically correct simulator approximately the size of a horse's heart with embedded dissection pathways was constructed and used with a series of laminated photographs of standardized cardiac dissection. A video of a standardized cardiac dissection of a normal horse's heart was recorded and presented with audio commentary. Students were allowed to nominate a preference for learning method, and students who indicated no preference were randomly allocated to keep group numbers even. Objective performance data from an objective structure assessment criterion and student perception data on confidence and competency from surveys showed both innovations were similarly effective. Evaluator reflections as well as usage logs to track patterns of student use were both recorded. A strong selection preference was identified for kinesthetic learners choosing the simulator and visual learners choosing the video. Students in the video cohort were better at articulating the reasons for dissection procedures and sequence due to the audio commentary, and student satisfaction was higher with the video. The major conclusion of this study was that both methods are effective tools for technical skills training, but consideration should be given to the preferred learning style of adult learners to maximize educational outcomes.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".