Mental Health Experiences and Service Use Among Veterinary Medical Students
Bibliographic record
Abstract
The mental health and wellness of veterinary students is an important contemporary focus of scholarship. Yet, to date, little empirical work has investigated mental health experiences and rates of mental health service use in large samples of veterinary students from multiple institutions. The purpose of this study is to explore the prevalence of mental health concerns among veterinary medical students, as well as rates of mental health service utilization, using validated measures and a large sample. Study participants were 573 veterinary medical students currently enrolled in accredited veterinary medical programs in the United States. Approximately one third of participants reported levels of depression or anxiety above the clinical cut-off, and a strong positive correlation was found between the two. Depression and anxiety were also associated with prior engagement in non-suicidal self-injury (NSSI), suicidal ideation, and prior suicide attempts. Nearly 80% of participants who scored above the clinical cut-off for depression or anxiety reported seeking some form of mental health services currently or in the past, and a majority reported having positive experiences with services. Results also indicated a higher than typical rate of NSSI among veterinary medical students. Implications for outreach, research, and education are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".