Empathy Levels among Veterinary Medicine Students in Colombia (South America)
Bibliographic record
Abstract
Empathy plays an important role in veterinarians’ relationships with their patients, clients, and colleagues. Because it relates to greater clinical competence and facilitates the acquisition of information for diagnosing, prescribing therapies, and identifying and treating animal pain, empathy is an essential competence to be strengthened during professional training. The objective of this study was to evaluate the empathy levels of veterinary medicine students toward people and animals and to identify associated factors. The animal empathy scale and the Davis interpersonal reactivity index were applied through an electronic survey to first-, third-, and fifth-year students ( n = 559) in three veterinarian medical schools in Colombia. A principal components analysis was performed to identify composite scores of human and animal empathy levels. The empathy toward humans total score ranged from 0 to 112, and the empathy toward animals total score was between 22 and 198. The average empathy scores for students were 89.67 ± 9.02 (mean ± SD; range: 60–115) and 115.01 ± 13.41 (mean ± SD; range: 67–165), respectively. The results suggest that empathy scores toward people are acceptable. Gender, university, program type, age, year of study, and diet were significantly associated with empathy levels toward animals. It is proposed that levels of empathy toward animals be strengthened by fostering a positive learning environment, developing ethical and animal welfare competencies, and increasing empathetic contact and hands-on experience with animals during the curriculum.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".