Turkish Undergraduate Veterinary Students’ Attitudes to Use of Animals and Other Teaching Alternatives for Learning Anatomy
Bibliographic record
Abstract
This study aimed to investigate the views of first-year veterinary students in Turkey from six veterinary faculties on their anatomy courses and to evaluate their perceptions of the uses of animals and other teaching alternatives from an ethical perspective. The study sample included a total of 293 veterinary students studying in the provinces of Ankara, Burdur, Diyarbakır, Kars, Konya, and Tekirdağ. The 38-item instrument tool developed by the researchers consisted of three sections and was administered to volunteer student participants. All the data were statistically analyzed, and normal distribution of the scores obtained in the attitude scales was determined using the Kolmogorov-Smirnov Z test (KSZ). The 20 items in the Anatomy Scale had an arithmetic mean of 3.48 and thus indicated an average rating of agree. The most challenging topic was found by 40.9% to be "the nervous system." The most useful material in facilitating the learning process was rated by 24.1% to be "the anatomy book." The 11 items in the Cadaver Scale had an arithmetic mean of 3.77, indicating an average rating of agree. The highest arithmetic mean score was for the item "Using cadavers is a must for the anatomy course" with a mean of 4.66, indicating their strong agreement with this view. The veterinary students' perspective emphasized that the combination of cadavers and the anatomy book contributed to their learning of anatomy.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".