Factors Influencing Veterinary Students’ Career Choices and Attitudes to Animals
Bibliographic record
Abstract
The purpose of the study was to investigate the influence of demographic and experiential factors on first-year veterinary students career choices and attitudes to animal welfare/rights. The study surveyed 329 first-year veterinary students to determine the influence of demographic factors, farm experience, and developmental exposure to different categories of animals on their career preferences and on their attitudes to specific areas of animal welfare and/or rights. A significant male gender bias toward food-animal practice was found, and prior experience with particular types of animals--companion animals, equines, food animals--tended to predict career preferences. Female veterinary students displayed greater concern for possible instances of animal suffering than males, and prior experience with different animals, as well as rural background and farm experience, were also associated with attitude differences. Seventy-two percent of students also reported that their interactions with animals (especially pets) had strongly influenced the development of their values. Animals ranked second in importance after parents in this respect. The present findings illustrate the importance to issues of animal welfare of the cultural context of past experience and influences on attitude development. The results also suggest that previous interactions with animals play a critical role in guiding veterinary students into their chosen career, as well as in helping to determine their specific employment preferences within the veterinary profession. From an animal welfare perspective, the dearth of women choosing careers in food-animal practice is a source of concern.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".