Feasibility and effectiveness of an online mindfulness meditation program for medical students
Bibliographic record
Abstract
BACKGROUND: The need to incorporate tools to promote medical student wellness in medical education is underscored by the concerning rates of psychological distress among them. The objective of this prospective cohort study was to obtain preliminary data on the feasibility and effectiveness of an online mindfulness intervention for medical student wellness. METHODS: A convenience sample of 52 medical students consented to participate in this study. Feasibility was assessed by ease of recruitment, number of modules completed, satisfaction with the program, and adherence to a regular meditation practice. Participants completed the Maslach Burnout Inventory, the Jefferson Scale of Empathy-medical student version, the Five Face of Mindfulness Questionnaire-short form, and the Self Compassion Scale-short form pre and post intervention. RESULTS: The convenience sample was recruited within a two-month period. Forty-five participants completed at least one of seven modules. Descriptive statistics (mean±standard deviation) revealed that the mean number of modules completed was 4.85±2.7. Mean satisfaction with the modules was 7.07±1.1 out of 10. Adherence to a regular formal meditation practice was poor; the average amount of formal meditation practice per module was 34.14±27.44 minutes. Self-compassion and the "observe and describe" facets of mindfulness practice significantly statistically increased from baseline, but no such change was observed for levels of burnout and empathy. CONCLUSION: The present study indicates that an online mindfulness meditation program may be of interest to medical students. The results did not provide any evidence that the program was effective but we believe further research and development is warranted.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".