A Study of Depression and Anxiety, General Health, and Academic Performance in Three Cohorts of Veterinary Medical Students across the First Three Semesters of Veterinary School
Bibliographic record
Abstract
This study builds on previous research on predictors of depression and anxiety in veterinary medical students and reports data on three veterinary cohorts from two universities through their first three semesters of study. Across all three semesters, 49%, 65%, and 69% of the participants reported depression levels at or above the clinical cut-off, suggesting a remarkably high percentage of students experiencing significant levels of depression symptoms. Further, this study investigated the relationship between common stressors experienced by veterinary students and mental health, general health, and academic performance. A factor analysis revealed four factors among stressors common to veterinary students: academic stress, transitional stress, family-health stress, and relationship stress. The results indicated that both academic stress and transitional stress had a robust impact on veterinary medical students' well-being during their first three semesters of study. As well, academic stress negatively impacted students in the areas of depression and anxiety symptoms, life satisfaction, general health, perception of academic performance, and grade point average (GPA). Transitional stress predicted increased depression and anxiety symptoms and decreased life satisfaction. This study helped to further illuminate the magnitude of the problem of depression and anxiety symptoms in veterinary medical students and identified factors most predictive of poor outcomes in the areas of mental health, general health, and academic performance. The discussion provides recommendations for considering structural changes to veterinary educational curricula to reduce the magnitude of academic stressors. Concurrently, recommendations are suggested for mental health interventions to help increase students' resistance to environmental stressors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".