Stressors and Protective Factors among Veterinary Students in New Zealand
Bibliographic record
Abstract
A study was undertaken to investigate the stressors faced by veterinary students and the protective factors against those stressors. The study was conducted as a workshop during which students collaborated with their peers through an iterative process to identify personal and external factors that contributed to or protected against stress as a veterinary student, and then to suggest strategies that would protect their mental health and well-being. Workload and assessment were the most commonly reported stressors. Students reported a variety of effective coping strategies and avoidance behaviors, although most of the suggested solutions revolved around organizational change within the university. Students also recognized that their own perspectives, traits, and behavior could enhance their student experience or increase their perceived levels of stress. While it is important that educators monitor student feedback about the program and make changes when required, students must recognize that stress is an expected component of life and develop effective coping strategies. They should develop a balanced view of the positive and negative aspects of the student experience and, ultimately, of working as a veterinary professional.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".