Pulling our lens backwards to move forward: an integrated approach to physician distress
Bibliographic record
Abstract
The medical community has recently acknowledged physician stress as a leading issue for individual wellness and healthcare system functioning. Unprecedented levels of stress contribute to physician burnout, leaves of absence and early retirement. Although recommendations have been made, we continue to struggle with addressing stress. One challenge is a lack of a shared definition for what we mean by 'stress', which is a complex and idiosyncratic phenomenon that may be examined from a myriad of angles. As such, research on stress has traditionally taken a reductionist approach, parsing out one aspect to investigate, such as stress physiology. In the medical domain, we have traditionally underappreciated other dimensions of stress, including emotion and the role of the environmental and sociocultural context in which providers are embedded. Taking a complementary, holistic approach to stress and focusing on the composite, subjective individual experience may provide a deeper understanding of the phenomenon and help to illuminate paths towards wellness. In this review article, we first examine contributions from unidimensional approaches to stress, and then outline a complementary, integrated approach. We describe how complex phenomena have been tackled in other domains and discuss how holistic theory and the humanities may help in studying and addressing physician stress, with the ultimate goal of improving physician well-being and consequently patient care.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".