Brief online mindfulness training for medical students: a randomized control study
Bibliographic record
Abstract
BACKGROUNDMedical students experience high levels of stress during their training. Literature suggests that mindfulness can reduce stress and increase self-compassion levels in medical students. However, most mindfulness training programs are delivered face-to-face and require significant time commitments, which can be challenging for rurally-based students with heavy academic workloads and limited support networks. PURPOSETo evaluate the feasibility and efficacy of a brief online Mindfulness training program for medical students based in rural areas, with regards to reducing stress, increasing self-compassion, mindfulness and study engagement. METHODSThis is a non-registered randomised control trial. Participants included both urban and rural medical students from UWA, University of Notre Dame and the RCSWA from 2018-2020. Participants were randomised to the intervention group, an 8-week online mindfulness training program, or the control group. Using quantitative-qualitative mixed-methods approach, we measured the frequency, duration and quality of the participants mindfulness meditation practice, and assessed changes in their perceived stress, self-compassion, mindfulness and study engagement levels. Further, the intervention group recorded a weekly reflective journal documenting their experience of the program. RESULTS114 participants were recruited to the study. 61 were randomised to the intervention, and 53 to the control. Quantitative analysis of the frequency, duration and quality of mindfulness meditation practice and changes in stress, self-compassion, mindfulness and study engagement is currently being conducted. Preliminary qualitative results reveal that participants experienced increased self-awareness, more mindfulness of their day-to-day activities, improved emotional regulation and increased productivity, while also facing difficulties with making time for their mindfulness practice. CONCLUSIONWe anticipate that this study will demonstrate that an online mindfulness training program tailored to reach rurally located medical students is feasible and effective in modifying their stress levels and psychological wellbeing.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".