Professional Coaching as a Continuing Professional Development Intervention to Address the Physician Distress Epidemic
Bibliographic record
Abstract
ABSTRACT: Physician distress and burnout are reaching epidemic proportions, threatening physicians' capacities to develop and maintain competencies in the face of the increasingly demanding and complex realities of medical practice in today's world. In this article, we suggest that coaching should be considered both a continuing professional development intervention as well as an integral part of a balanced and proactive solution to physician distress and burnout. Unlike other interventions, coaching is intended to help individuals gain clarity in their life, rather than to treat a mental health condition or to provide advice, support, guidance, or knowledge/skills. Certified coaches are trained to help individuals discover solutions to complex problems and facilitate decision-making about what is needed to build and maintain capacity and take action. Across many sectors, coaching has been shown to enhance performance and reduce vulnerability to distress and burnout, but it has yet to be systematically implemented in medicine. By empowering physicians to discover and implement solutions to challenges, regain control over their lives, and act according to their own values, coaching can position physicians to become leaders and advocates for system-level change, while simultaneously prioritizing their own well-being.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".