The Impact of a Brief Embedded Mindfulness-Based Program for Veterinary Students
Bibliographic record
Abstract
Veterinary medical students, like other university students, are likely to experience elevated levels of stress, anxiety, and depression over the course of their studies. Mindfulness-based interventions have previously been effective for university students in reducing stress, depression, and anxiety. In this study, a mindfulness-based intervention was embedded in a core (compulsory) unit of a veterinary science course, in part with the aim of improving student well-being. Preliminary results suggest that, despite the mindfulness intervention, overall symptoms of stress, depression, and anxiety among participants (n=64) increased between the start and end of the semester. However, further analysis showed that most of this longitudinal increase was attributable to individuals who scored above the normal range (i.e., at least mild level of symptoms) in one or more measures at the beginning of the semester. Within this subset, individuals who regularly engaged in mindfulness practice once a week or more throughout the semester reported significantly lower depression and anxiety symptoms than those who practiced less than once a week (i.e., who had long periods without practice). Results suggest that engaging regularly in mindfulness practice potentially acted as a protective factor for students already experiencing at least a mild range of symptoms of anxiety and depression at the beginning of the semester. While not all veterinary students may derive significant benefit immediately, providing access to an embedded mindfulness program early in their program may facilitate the development of adaptive coping mechanisms, which may be engaged to increase resilience across their academic and professional life.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".