Enhancing Clinicians' Well-Being and Patient-Centered Care Through Mindfulness
Bibliographic record
Abstract
INTRODUCTION: Mindful clinicians are resilient and more likely to provide patient-centered care. We aimed to enhance clinicians' well-being by offering a Mindfulness-Based Stress Reduction (MBSR) course that teaches mindfulness and stress management and then determine whether this impacted their subsequent medical encounters. METHODS: In a longitudinal cohort study with 27 clinicians, MBSR was taught by a certified instructor. Pre-MBSR and post-MBSR online questionnaires assessed burnout, depression, stress, meaningfulness, and mindfulness. Patients independently rated their clinicians using the Rochester Communication Rating Scale (RCRS) after a clinical encounter before and after their clinician took the MBSR course. Nine medical doctors audiorecorded the consultations before and after MBSR; the tapes were coded and analyzed by an independent team using the Roter interaction analyses system. RESULTS: Significant reductions in stress and burnout were found, and increases in mindfulness and meaningfulness. The decrease in stress was correlated with less judgmental attitudes and less reactivity-facets of mindfulness. The decrease in emotional exhaustion was correlated with more acting with awareness and less judgmental attitudes-facets of mindfulness. Patients' perceptions of the clinical encounter suggested that patient-centered care improved after MBSR. Decreased depersonalization was significantly associated with the RCRS subscale, "understanding of the patient's experience of illness." At both time points, doctors dominated the exchange and were patient-centered. DISCUSSION: Mindfulness has a direct and positive impact on clinicians' well-being. When clinicians' experienced less depersonalization, their patients reported being better understood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".