Stressors and Stress of Veterinary Students during their Introduction to the Clinical Workplace
Bibliographic record
Abstract
Various stressors contribute to veterinary students’ stress levels. According to the medical education literature, students’ stress seems to increase during clinical training, but research investigating this in veterinary students is scarce. According to transactional stress theory, individual students may not perceive every stressor as equally stressful. The present research therefore aimed to investigate how stressful veterinary students perceive stressors of clinical training, identify subgroups based on their perceptions of these stressors, and determine whether the subgroups differ regarding their total clinical training-related stress and academic achievement. The sample consisted of 197 veterinary students completing their clinical rotation course. The Rotation Stress Questionnaire for Veterinary Students (RSQV) was employed to assess rotation-specific stressors and stress. Course grades served as indicators of academic achievement. Veterinary students reported moderate overall clinical training-related stress, and heavy workload was the main source of stress. Hierarchical cluster analysis identified four subgroups of students, namely: the Generally Stressed Group, Responsibilities Uncertainty Group, Overtasked Group, and Unstressed Group, with significant differences in total stress ( p < .001). The groups also differed significantly in academic achievement ( p = .015), with post-hoc analysis indicating significant mean differences between the highest- and lowest-stress groups ( p = .014). In conclusion, veterinary students’ stress during clinical training appears to be a significant factor, particularly concerning workload. However, there are interindividual differences in total stress and achievement which should be considered.
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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 | 0.003 |
| 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 | 0.000 |
| 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".