Integrating Same-Level Peer-Assisted Learning in a Simulation-Based Emergency Dystocia Module for Final-Year Veterinary Students
Bibliographic record
Abstract
Due to faculty shortages, time restraints, and unpredictability of emergency cases, teaching emergency veterinary care is associated with a range of challenges. A novel simulation-based emergency veterinary care (EVC) module was introduced at the Department of Veterinary Medicine, Freie Universität Berlin. The module was mandatory for all final-year veterinary students ( n = 155) and consisted of a 5-hour online workshop series on communication skills, a series of interactive, virtual emergency cases, and a weeklong block event covering practical skills at different simulation-based learning stations. A same-level peer-assisted learning (PAL) approach was trialed at two learning stations. Sixteen students volunteered to act as student tutors for their peers. The student tutors received specific training and each tutored six groups of three to four tutees in one topic of their choice. Evaluation forms were filled out by both tutors and tutees with response rates of 100% and 89.7%, respectively. Most student tutors felt well prepared and comfortable in their role as tutor. They indicated exceptionally high levels of motivation and felt useful and competent during the exercise. The tutees reciprocated these opinions and specifically enjoyed the fun and positive learning environment that the tutors were able to create. Responses in the evaluation forms also indicated that the ratio of faculty member to tutors to tutees (1:3:9–12) was a good fit for the exercise. Reciprocal same-level PAL shows promise as an effective teaching tool for final-year veterinary students receiving EVC training. This translation was provided by the authors. To view the full translated article visit: https://doi.org/10.3138/jvme-2022-0038.de
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".