Over-the-Counter Stimulant, Depressant, and Nootropic Use by Veterinary Students
Bibliographic record
Abstract
US veterinary students are subject to significant stress throughout their veterinary education. In this article, the authors characterize the use of over-the-counter (OTC) medications and relate their use to stress in a veterinary student population. Of the students sampled, 35% were OTC medication users; 33% of these were regular OTC medication users. Forty-three percent of students were energy drink (ED) users; 45% of these were regular ED users. OTC medication users had significantly higher stress scores than non-OTC medication users, and ED users had significantly higher anxiety scores than non-ED users. The most common reasons for use given by OTC medication users were to help with studying and to fall asleep at night. Depression scores were significantly higher for juniors and sophomores than for freshmen. Depression, stress, and anxiety scores were all lower in the Colorado State University students when compared with the University of Georgia students. OTC medication and ED veterinary student users had distinct characteristics that differed from those of nonusers. Users suffered from more stress and anxiety and had more difficulties with sleep, which may have affected their overall health and academic performance. Educating veterinary students about the consequences of using OTC medication and ED and providing counseling support may be of benefit to veterinary students' psychological 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".