Mental Health in Swiss Veterinary Medicine Students: Variables Associated with Depression Scores
Bibliographic record
Abstract
Mental health and well-being in veterinary students has become an important area of study, given the high levels of mental distress compared to other professions. Although research has identified poor mental health of veterinary students, few studies have examined positive factors such as self-compassion, which can have a favorable effect on mental health. The aim of this study was to examine self-reported symptoms of depression and factors influencing this construct, such as loneliness, self-compassion, and various demographic aspects in Swiss veterinary students. A sample of 374 Swiss veterinary students completed online measures including a demographic questionnaire, the Center for Epidemiological Studies Depression Scale-Revised (CESD-R), a short form of the University of California Loneliness Scale (UCLA-9), and the short form of the Self-Compassion Scale (SCS-SF). Results showed that 54.3% of the students were above the CESD-R cut-off score for depression, indicating a higher likelihood to suffer from depression. Results from the backward selection linear regression showed loneliness to be a risk factor, while self-compassion and rural upbringing were protective of depression. Findings suggest that Swiss veterinary students are highly burdened and may benefit from interventions aimed at reducing loneliness and improving self-compassion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".