Piloting a Mindfulness-Based Intervention to Veterinary Students: Learning and Recommendations
Bibliographic record
Abstract
Veterinary students experience high levels of psychological distress including anxiety, stress, perceived stress and depression. The inability to cope with the demands of veterinary training has personal and professional consequences. Existing evidence shows that mindfulness-based interventions (MBI) can reduce stress in students, but more research on how MBIs are introduced into the veterinary curriculum is required. The first aim of the pilot study was to design and deliver a bespoke MBI to third-year veterinary students at the University of Liverpool Institute of Veterinary Science. The second aim was to gain feedback from those taking part, thus using their experiences to explore the challenges of introducing an MBI into a veterinary curriculum. By doing this, we aim to reflect and learn for future interventions. Qualitative feedback provided by participants of the MBI focus group was analyzed using thematic analysis and organized into two main themes: (1) "Taking Part in the MBI and Beyond-What it Was Like and What Has the MBI Done for Me?" and (2) "Mindfulness for Veterinary Students-Reflections, Challenges, and Making it Happen." Experiences and outcomes of the MBI were positive. However, implementation into the veterinary curriculum was found to be challenging. This pilot study provides clear recommendations to support the future integration and delivery of MBIs into a veterinary curriculum.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".