The feasibility of an innovative online mind-body wellness program for medical students
Bibliographic record
Abstract
Purpose: Medical students have higher stress levels than their age-matched peers; however, few online wellness interventions have been trialed in this population. This pilot study examined the feasibility of an online wellness program for medical students. Method: This was a pilot feasibility, mixed-methods study with a pre-post design. From September to October 2020, medical students from a large Canadian medical school were introduced to a 12-week online program with weekly sessions on yoga, breathwork, meditation, and nutrition. Feasibility measures included adherence, satisfaction, and retention, with secondary outcome measures including stress, anxiety, quality of life, and mindfulness. Post-program qualitative interviews explored participant experience. Results: Of 74 participants, 64 completed the program. Twenty-one achieved the program goal of participating at least two days per week. While 74.8% of participants found the program accessible and satisfying, some students reported challenges with adherence. Exploratory analysis indicated there are signals for reduced stress (11%, p = 0.005), anxiety (14%, p = 0.001), and improved mindfulness (5.6%, p = 0.001). Qualitative analysis revealed themes of participants experiencing an increased sense of balance and mindfulness. Conclusion: A 12-week online wellness intervention appears feasible for medical students, showing potential benefits for stress, anxiety, and mindfulness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".