Self in medicine: Determinants of physician well‐being and future directions in improving wellness
Bibliographic record
Abstract
CONTEXT: Medicine, a profession dedicated to the wellness of patients, is struggling with a crisis of physician and trainee wellness. Physicians and trainees are burning out at alarming rates. Historically, medicine has been characterised by challenging working conditions and inattention to physician wellness and self-care. Healthy physicians are better at promoting wellness to their patients, and physicians who are suffering from burnout can deliver compromised patient care. DISCUSSION: In recent years, research has increasingly focused on the causes of unwellness among doctors, and a broad range of health determinants have been identified. Studies of interventions for improving trainee and physician wellness have identified individual-focused and organisational approaches that can address the root causes of burnout. Insights from the corporate workplace may help guide interventions. Strategies for addressing physician burnout and improving wellness will involve innovative and multifaceted approaches. Despite a growing emphasis of physician wellness in the literature, implementation of wellness interventions is lagging, and quality improvement methods can address these challenges. CONCLUSION: Physician wellness is a shared responsibility among doctors, health care organisations and governing bodies. Addressing burnout and improving physician wellness will require transformational change and the embracement of a culture of wellness in medicine. Quality improvement methods are the next step in identifying effective wellness interventions and the targeted groups of physicians and trainees who will most benefit.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".