Optimizing Compassion Training in Medical Trainees Using an Adjunct mHealth App: A Preliminary Single-Arm Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: While structured compassion training programs have shown promise for increasing compassion among medical trainees, a major challenge is applying the concepts and practices taught during the program into the complex, dynamic, time-pressured, and often hectic hospital workplace. OBJECTIVE: The purpose of this pilot study was to examine the feasibility, acceptability, and preliminary effects of Compassion Coach, a mobile health (mHealth) smartphone app designed to bolster a 6-week mindfulness and self-compassion training program for medical trainees. METHODS: In Compassion Coach, notifications to remind, encourage, and measure the perceived impact of informal mindfulness and compassion practices taught during the program were delivered at 7 AM, 12 PM, and 7 PM, respectively, 3 times per week over the course of the training program. The app also contained a library of guided audio formal mindfulness and compassion practices to allow quick and easy access. In this pilot study, we collected data from 29 medical students and residents who downloaded Compassion Coach and completed surveys assessing perceived effectiveness and acceptability. Engagement with the Compassion Coach app was passively tracked through notification response rate and library resource access over time. RESULTS: The average response rate to notifications was 58% (SD 29%; range 12%-98%), with a significant decline over time (P=.009; odds ratio 0.98, 95% CI 0.96-0.99). Across all participants and occasions, the majority agreed the informal practices prompted by Compassion Coach helped them feel grounded and centered (110/150, 73%), improved compassion (29/41, 71%), reduced burnout (106/191, 56%), and improved their mood (133/191, 70%). In total, 16 (55%) of the 29 participants accessed guided audio recordings on average 3 (SD 3.4) times throughout the program. At the posttreatment time point, most participants (13/18, 72%) indicated that Compassion Coach helped them engage in compassion practices in daily life, and half (9/18, 50%) indicated that Compassion Coach helped improve interactions with patients. CONCLUSIONS: Overall, preliminary results of Compassion Coach are encouraging and suggest the integration of a smartphone app with an ongoing mindfulness and self-compassion training program may bolster the effects of the program on medical trainees. However, there was variability in engagement with Compassion Coach and perceived helpfulness. Additional research is indicated to optimize this novel mHealth approach and conduct a study powered to formally evaluate effects.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".