Stress in Veterinary Science Students: A Study at the University of Queensland
Bibliographic record
Abstract
This paper reports on the results of a survey of selected University of Queensland (UQ) veterinary students aimed at elucidating factors causing stress during the five undergraduate years of the program. Students from each of the five years were asked to form six- or seven-member focus groups. Each focus group was then interviewed and their opinions sought on causes of ongoing stress and the ranking of those causes into predetermined categories. They were also asked to give their opinions on counseling services available within the university and what, if any, services they would like to see in place to help students with stress-related problems. Students in the first, third, and fourth years of the program rated academic issues as the most likely causes of ongoing stress, while students in the second and fifth years of the program ranked lifestyle and financial issues as more likely to cause ongoing stress. In most cases, students coped well with these causes of stress and tended not to use counseling services available to all UQ students. When faced with stressful issues, students looked to their classmates or family members for help and not to university counseling services. Students were also happy to approach staff members in the Veterinary School when faced with a problem. The authors nevertheless conclude that mechanisms set in place at the undergraduate level to help veterinary students cope with stress should particularly benefit those students when they become new graduates and are faced with the stresses of veterinary practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".