“Obligated to Keep Things Under Control”: Sociocultural Barriers to Seeking Mental Health Services Among Veterinary Medical Students
Bibliographic record
Abstract
Research reveals veterinary medical students and professionals are at increased risk for mental health problems such as depression, anxiety, and suicidality, yet many individuals in distress do not seek professional mental health services. Although some barriers to accessing services have been identified, other factors, including how professional culture influences service underutilization, are poorly understood. In this study, we used a mixed-methods approach to investigate 573 veterinary students' perceptions of barriers to seeking mental health services and potential mechanisms to lessen them. We identified four barrier themes: stigma, veterinary medical culture and identities, services, and personal factors. Participants' suggestions for reducing barriers to seeking help related to three themes: culture, services, and programmatic factors. We compared perceptions of barriers based on the severity of participants' self-reported symptoms of depression and anxiety and found that participants with severe depression, compared with participants with mild depression, were more likely to perceive barriers related to veterinary medical culture. The results of this study provide a deeper understanding of veterinary students' barriers to seeking mental health services and, in particular, how these barriers, as both individual and sociocultural phenomena, are often interrelated and mutually reinforcing.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".