Mental Health Stigma and Veterinary Medical Students’ Attitudes Toward Seeking Professional Psychological Help
Bibliographic record
Abstract
Veterinary medical students may be at increased risk for a variety of mental health problems. However, research with student samples suggests that students in distress may not seek professional help, even when mental health services are available. The purpose of this study was to explore veterinary students' willingness to seek mental health services for several common presenting concerns, as well as their perceptions of their peers' willingness to seek help for the same concerns. We also sought to explore the roles of public stigma, self-stigma, and attitudes toward seeking professional psychological help in explaining students' willingness to seek services using a serial mediation analysis. Study participants were 573 veterinary medical students currently enrolled in accredited programs in the United States. Participants reported being most willing to seek mental health services for issues regarding substance abuse, traumatic experiences, and anxiety. They also tended to perceive other students were less willing to seek mental health services for most presenting issues than they actually were. As expected, self-stigma and attitudes toward seeking professional psychological help serially mediated the relationship between public stigma and willingness to seek mental health services. Public stigma was positively related to self-stigma, self-stigma was negatively related to attitudes toward seeking professional psychological help, and attitudes toward seeking help were positively related to willingness to seek mental health services. 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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".