Mindful practice in medicine: A global program to reduce burnout and improve healthcare quality
Bibliographic record
Abstract
Introduction: The growing prevalence of burnout among healthcare professionals has emerged as a global crisis, adversely affecting individual well-being, patient care, and healthcare systems while imposing significant economic burdens. Addressing this systemic problem requires innovative, scalable interventions that target the root causes of burnout. Mindful Practice in Medicine (MPIM), developed at the University of Rochester School of Medicine and Dentistry, represents a promising approach. MPIM fosters self-awareness, emotional intelligence, teamwork, and compassion. With over 20 years of evidence-based implementation, MPIM has demonstrated substantial improvements in clinician well-being, burnout, empathy, teamwork, and patient-centered care. Methods: This global perspective highlights the program’s global impact through case studies of MPIM-trained facilitators who have embeded these programs into undergraduate, graduate, and postgraduate medical education as well as into institutional healthcare systems. Results: Examples from Switzerland, the United States, the United Kingdom, Australia, and Canada illustrate MPIM’s adaptability and effectiveness for fostering systemic cultural changes, restoring joy in medicine, and promoting organisational resilience. Conclusion: These efforts underscore the potential of MPIM to catalyse a global paradigm shift in healthcare, improving outcomes for both professionals and patients. Further research and strategic scaling are necessary to maximise MPIM’s reach and sustainability and to address the intertwined crises of professional burnout and healthcare quality.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".