Physician Burnout and Occupational Stress: An inconvenient truth with unintended consequences
Bibliographic record
Abstract
Healthcare providers and staff are the proximal source of quality of care provided to patients. Today’s world of health carereform and other value-based initiatives have added new levels of significant complexity to health care delivery. This cumulativechronic high-level stress is imposed by multiple regulatory, insurance, federal, and state forces that do not coordinate well withone another resulting in disparate, conflictual, or confusing mandates. Each have authoritative capital. Together they havepotential to affect healthcare workers on a personal, physical, emotional and cognitive level which in turn adversely affects carerelationships and quality of patient care. We need to be concerned about the effect that this enormous occupational stress hason them as individuals and how it impacts the care provided. Physician shortages exist and are projected to get worse. There isa high burnout rate in current physicians. Some are retiring early, leaving medicine, or worse dying of suicide from job relatedstress. Mechanisms of this negative effect of stress and Burnout on providers, institutions and healthcare quality are discussed.The aim of this paper is to provide an overview of current state of knowledge merging information from various fields on thisissue. Areas that require action are identified and possible solutions are offered.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".