Associations among Depressive Symptoms, Drinking Motives, and Risk for Alcohol-Related Problems in Veterinary Students
Bibliographic record
Abstract
Hazardous alcohol consumption among medical students appears to occur at a level comparable to the general population; however, among medical students, it has been found that the motivation to use alcohol partially stems from unique stressors related to their professional training. Although veterinary students may also experience psychological distress in association with their training, little work has focused on the way that these students use alcohol to cope with their distress. The current study sought to examine the severity of depressive symptoms and alcohol consumption among veterinary students as well as students' specific motives for drinking alcohol. The majority of our sample reported experiencing at least one depressive symptom, and a significant proportion engaged in high-risk drinking, with men reporting more harmful alcohol use patterns. Drinking motives related to managing internal bodily and emotional states accounted for variance in drinking patterns. Further, drinking to ameliorate negative emotions partially accounted for the relationship between psychological distress and high-risk drinking. The results of this study suggest that depressive symptoms among veterinary students may be related to harmful drinking patterns, due to alcohol being used as a coping mechanism to regulate emotions. The findings from this study can be used to develop targeted interventions to promote psychological well-being among veterinary students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".