A New Educational Resource to Improve Veterinary Students' Animal Welfare Learning Experience
Bibliographic record
Abstract
A computer-aided learning (CAL) educational resource based on experiential learning principles has been developed. Its aim is to improve veterinary students' ability to critically review the effect on welfare of husbandry systems observed during their work placement on sheep farms. The CAL consisted of lectures, multiple-choice questions, video recordings of animals in various husbandry conditions, open questions, and concept maps. An intervention group of first-year veterinary students (N=31) was selected randomly to access the CAL before their sheep farm placement, and a control group (N=50) received CAL training after placement. Assessment criteria for the categories remember, understand, apply, analyze, evaluate, and create, based on Bloom's revised taxonomy, were used to evaluate farm reports submitted by all students after their 2-week placement. Students in the intervention group were more likely than their untrained colleagues (p<.05) to remember and understand animal-based measurements relating to the freedom from hunger and thirst; the freedom from discomfort; and the freedom from pain, injury, or disease. Intervention group students were also more likely to analyze the freedom from pain, injury, or disease and the freedom to exhibit normal behavior and to evaluate the freedom from fear and distress. Relatively few students in each group exhibited creativity in their reports. These findings indicate that use of CAL before farm placement improved students' ability to assess and report animal welfare as part of their extramural work experience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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; both teacher heads agree on what is shown here.
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".