Assessment of Progressive Muscle Relaxation (PMR) as a Stress-Reducing Technique for First-Year Veterinary Students
Bibliographic record
Abstract
The veterinary profession continually strives to address wellness issues such as compassion fatigue, burnout, stress, anxiety, and depression. Wellness issues may begin during the professional curriculum when students experience intense academic, clinical, social, and personal demands on their time. The purpose of this article was to assess the use of progressive muscle relaxation (PMR) as a simple, non-invasive stress reduction technique for first-year veterinary students ( n = 101) at a US veterinary college. Students completed a 38-item questionnaire, the Smith Relaxation States Inventory 3 (SRSI3), both before and after performing PMR. Scores for the categories of basic relaxation, mindfulness, positive energy, transcendence, and stress were assessed. Female students ( n = 92) had significant ( p < .05) improvement in basic relaxation, mindfulness, and stress after completing PMR. Male students ( n = 9) had significant ( p < .05) improvement in basic relaxation and stress after completing PMR. When grouped according to age, all students had significant ( p < .05) improvement in the categories of basic relaxation and stress. Students in the 22-year-old ( n = 31), 23-year-old ( n = 29), 24-year-old ( n = 15), and 25-year-old or greater ( n = 17) groups also had significant improvement ( p < .05) in mindfulness. Additionally, students in the 23-year-old group had significant ( p < .05) improvement in positive energy. These results support the use of PMR as a potential self-care strategy for students to implement during their academic and professional careers.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".