The First-Year Veterinary Student and Mental Health: The Role of Common Stressors
Bibliographic record
Abstract
The present study evaluated the impact of academic and non-academic stressors on depression levels in a longitudinal investigation of 78 first-year veterinary medical students enrolled at Kansas State University (KSU). Students completed the Center for Epidemiological Studies Depression Scale during their first and second semesters to evaluate the dependent variable, depression. Students provided information about specific stressors and relevant demographic variables that yielded independent variables. One-third of veterinary medical students surveyed in their first and second semesters reported depression levels above the clinical cut-off; 15% of the sample experienced an increase in depression of at least one standard deviation, despite the apparent stability of the proportion of students experiencing significant depressive symptoms. Students whose depression scores increased by one standard deviation or who maintained scores above the clinical cut-off score were identified as struggling. Struggling students reported more first-semester homesickness and academic concerns, along with difficulty fitting in with peers and poorer perceived physical health during the second semester. This study helped to identify those students most prone to develop or maintain concerning depression scores. The discussion section addresses specific suggestions for intervening with struggling students.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".