Challenges and potential solutions for physician suicide risk factors in the COVID-19 era: psychiatric comorbidities, judicialization of medicine, and burnout
Bibliographic record
Abstract
INTRODUCTION: Suicide among physicians constitutes a public health problem that deserves more consideration. A recently performed meta-analysis and systematic review evaluated suicide mortality in physicians by gender and investigated several related risk factors. It showed that the post-1980 suicide mortality was 46% higher in female physicians than among women in the general population, while the risk in male physicians was 33% lower than among men in general, despite an overall contraction in physician mortality rates in both genders. METHODS: This narrative review was conducted by searching and analyzing articles/databases that were relevant to addressing questions raised by a prior meta-analysis and how they might be affected by COVID-19. This process included unstructured searches on Pubmed for physician suicide, burnout, judicialization of medicine, healthcare organizations, and COVID-19, and Google searches for relevant databases and medical society, expert, and media commentaries on these topics. We focus on three factors critical to addressing physician suicides: epidemiological data limitations, psychiatric comorbidities, and professional overload. RESULTS: We found relevant articles on suicide reporting, physician mental health, the effects of healthcare judicialization, and organizational involvement on physician and patient health, and how COVID-19 may impact such factors. This review addresses information sources, underreporting/misreporting of physician suicide rates, inadequate diagnosis and management of psychiatric comorbidities and the chronic effects on physicians' work capacity, and, finally, judicialization of medicine and organizational failures increasing physician burnout. We discuss these factors in general and in relation to the COVID-19 pandemic. CONCLUSIONS: We present an overview of the above factors, discuss possible solutions, and specifically address how COVID-19 may impact such factors.
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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 | 0.035 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".