Applied Mindfulness for Physician Wellbeing: A Prospective Qualitative Study Protocol
Bibliographic record
Abstract
BACKGROUND: Physician burnout has significant adverse impacts on the wellbeing of individual physicians, and by extension the healthcare delivery systems of which they are part. Mindfulness is consistently cited as a pragmatic approach to effectively address burnout and enhance physician wellbeing. However, very few empirical studies have been published on Mindfulness Based Interventions (MBIs) for physicians. Moreover, the majority of these studies have been quantitative, leaving a gap in understanding the practical application of mindfulness in the context of physicians' daily lives. OBJECTIVES: This paper outlines the rationale, development and design of a novel prospective qualitative study examining the acceptability, feasibility, and pragmatic application of a mindfulness intervention for physician wellness. METHODS: The study will be conducted in three groups of at least 8 practicing physicians from a broad range of medical specialties at a tertiary care hospital in a large urban center in Eastern Canada. The intervention will consist of an innovative program based on the teachings of internationally renowned scholar and Zen Master Thích Nhãt Hạnh. It will include 5 weekly 2-h mindfulness sessions delivered by two health providers trained in mindfulness and in the teachings of Thich Nhat Hanh. The primary outcome measure will be an in-depth Thematic Analysis of post-program semi-structured interviews. Field data will also be collected through participant observation. The study will be theoretically grounded within the interpretive paradigm utilizing "the Mechanisms of Mindfulness Theory". An external advisory committee formed by four senior members of Thích Nhãt Hạnh's community will provide guidance across all phases of the study. DISCUSSION: Our innovative approach provides a new framework to further understand the mechanisms by which mindfulness interventions can impact physician wellbeing, and by extension their patients, colleagues, and broader healthcare systems. Through a clear articulation of the rigorous application of accepted procedures and standards used in our protocol, this paper seeks to provide a roadmap for other researchers who wish to develop further studies in this area. Lessons learned in the preparation and conduction of this study can be applied to other healthcare contexts including non-physician health provider wellbeing, clinical care, and population-level mental health.
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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 | 0.063 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".