Assessing the Mental Well-Being and Help-Seeking Behaviors of Pre-Veterinary Undergraduates at a Land-Grant Institution
Bibliographic record
Abstract
Recent research conducted within the veterinary profession has reported higher rates of depression and stress than the general US population. While this decline in mental well-being has been documented in Doctor of Veterinary Medicine (DVM) students and veterinary professionals, there is a lack of research on the mental well-being of the pre-veterinary population. This gap led us to conduct a survey in the fall of 2021 utilizing the DASS-21 and ATSPPH-sf inventories to assess the levels of depression, anxiety, stress, and help-seeking stigma in pre-veterinary students to better understand when the decline in veterinary mental well-being begins. A pre-test survey was completed by 233 pre-veterinary students in September, and an identical post-test survey was completed by 184 pre-veterinary students in November. From the pre- and post-test data, depression, anxiety, and stress scores increased as students advanced in academic status during their undergraduate degree. Juniors reported the highest averages of depression, anxiety, and stress compared with their peers. In the post-test, sophomores and juniors exhibited higher rates of depression than freshmen, and juniors and seniors exhibited higher rates of stress than freshmen. Current VMCAS applicants exhibited higher levels of stress than non-VMCAS applicants in the pre-test and lower levels of stress in the post-test. In both the pre-test and post-test data, respondents averaged a neutral attitude toward help-seeking. Based on these results, a decline in pre-veterinary mental well-being occurs as students' progress in their undergraduate career and should be further studied to assess its impact on Doctor of Veterinary Medicine and veterinary professional well-being.
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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) | 0.003 | 0.001 |
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".