Mindfulness-Based Medical Practice: Exploring the Link between Self-Compassion and Wellness
Bibliographic record
Abstract
Objectives: In light of the detrimental impact of burnout upon clinicians and their patients, the identification of means through which the well-being of health care professionals can be fostered and protected is timely and important. The present study explored outcomes associated with participation in Mindfulness-Based Medical Practice (MBMP), a program modeled after Mindfulness-Based Stress Reduction which included additional mindful communication exercises to foster the integration of mindfulness in various clinical settings.Methods: Physicians, nurses, psychologists, occupational therapists, and social workers enrolled in the 8-week MBMP program. Participants (N = 110) between the age of 24 and 82 years (M = 46.5, SD = 11.4: 73% women) completed self-report measures prior to and following the program; the Maslach Burnout Inventory, Perceived Stress Scale-10 and the Ryff Scales of Psychological Well-Being. Two process measures designed to capture mechanisms of change were administered: the Mindful Attention Awareness Scale, and the Neff Self-Compassion Scale.Results: Results from paired-sample t-tests indicated that health care professionals enrolled in MBMP can benefit from the program. Analyses demonstrated significant decreases upon measures of perceived stress [p= .000], emotional exhaustion [p= .000], depersonalization [p= .000], and an increase in personal accomplishment [p= .000] as well as mindfulness [p=.000], self-compassion [p= .000], and well-being [p= .000]. Hierarchical regression analyses indicated that change scores on perceived stress (Beta = -1.46, p LT 0.000) and self-compassion (Beta = 9.02, p LT 0.006) predicted changes in well-being in this sample. Additionally, participants rate perceived importance of having taken part in the course using a Likert-scale from 1-10 (M=8.5, SD = 1.51).Conclusions: This study suggests that for health care professionals enrolled in MBMP may experience a variety of benefits associated with participation in the program. Further, increases in self-compassion may hold particular implications for well-being in this population.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".