Investing in compassion: exploring mindfulness as a strategy to enhance interpersonal relationships in healthcare practice
Bibliographic record
Abstract
Healthcare is a human enterprise where provider-patient interactions are a critical part of the therapeutic process. Unfortunately many healthcare providers are at risk of burnout or compassion fatigue that can detract from quality care. Mindfulness-based interventions have proven efficacy for reducing stress among healthcare workers, but there is limited evidence regarding its impact on interpersonal communication. The purpose of this mixed-methods, non-randomized intervention study was to track the inter-personal impact of a nine-week mindfulness-based stress reduction (MBSR) program on healthcare employees in two large hospitals. Pre and post group surveys were completed by 125 participants, tracking changes in empathy and symptoms of burnout, as well as gathering feedback about the program. Focus groups were also conducted with a sample of 12 participants one year later to explore their impressions of the sustained impact of the program. Analysis of the survey data indicated a significant increase in both cognitive and emotional dimensions of empathy, as well as significant decrease in the indicators of burnout. Many participants described an increased ability to listen mindfully to others, and that they were more tolerant and compassionate, with less emotional reactivity and better skills in managing conflict. Focus group participants indicated that they were able to integrate and apply principles of mindfulness into their day-to-day communications both within and outside of work. The findings provide a compelling argument for the value of mindfulness in not only building resilience, but enhancing communication in the context of healthcare work.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".