“Suicide Risk Among Physicians in the USA: A Systematic Narrative Review of Incidence, Risk Factors, and Prevention Strategies”
Bibliographic record
Abstract
ABSTRACT Physician suicide is a significant public health problem, as previous research shows physicians are at increased risk of dying by suicide compared to the general population. We aim to focus on the recent trends in physician suicide death rates, risk factors and preventive measures related to physicians in the US. We adhered to the PRISMA guidelines for systematic reviews. A search of PubMed, Embase, and PsycINFO resulted in 5139 records. We included 5 studies that provide a sex‐specific examination of physician suicide death rates, related risk factors, or preventive measures. We used the Newcastle‐Ottawa Scale for quality assessment and employed a thematic approach to interpret data. Our qualitative analysis revealed that female physicians have an elevated risk of suicide in comparison to their female non‐physician counterparts, whereas male physicians exhibit lower risk relative to male non‐physicians. Our findings show that male risk factors for suicide include job and legal stressors, while females were affected by mental health issues. Depression was a direct contributor to suicidal thoughts, while burnout was indirectly involved. Distinguishing between burnout and depression is essential for the implementation of successful preventative methods. Future research must investigate intersectional elements, as well as longitudinal post‐pandemic trends, to inform the formulation of fair policy. These findings underscore the critical need for supportive workplace conditions to mitigate suicide risk among physicians. Enhancing awareness of the stigma associated with mental health care access and prioritizing support networks are crucial measures for cultivating a culture of psychological well‐being within the medical profession.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".