Improving physician wellness through the Applied Mindfulness Program for Medical Personnel: findings from a prospective qualitative study
Bibliographic record
Abstract
BACKGROUND: Physicians play a critical role across health care delivery systems, yet their own well-being is often overlooked; mindfulness has been widely recommended as a promising modality to support physician wellness. We sought to explore how physicians experience and engage with a 5-week applied mindfulness program and how they perceive its impact on their personal well-being in the context of their daily lives. METHOD: We delivered the Applied Mindfulness Program for Medical Personnel (AMP-MP) at a tertiary care hospital in downtown Toronto, Canada. This prospective qualitative study consists of a thematic analysis of post-program interviews with physicians, from across different specialties, who participated in the AMP-MP. The program includes 2-hour sessions, delivered once a week over 5 weeks, and is based on the teachings of Thích Nhất Hạnh. RESULTS: We interviewed 28 physicians after they completed the AMP-MP. Our data show that a 5-week training was sufficient for physicians to develop a foundational level of mindfulness that integrated into their daily life. Two themes were identified: mindfulness encourages behavioural and cognitive changes that facilitate well-being, and mindfulness improves communication with patients and colleagues. INTERPRETATION: Our results show applied mindfulness to be well received by physicians as an effective modality to increase their perceived sense of wellness and enhance communication with their patients and colleagues. Further research is necessary to better understand the individual and systemic implications of mindfulness training, and how this modality can complement other efforts being made to address and maintain physician wellness.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".