Causes of Death Among Health Care Professionals in the United States
Bibliographic record
Abstract
Specific causes of mortality among various types of health care professionals (HCPs), including those characterized by age, gender, and race, have not been well described. The National Occupational Mortality Surveillance data for deaths in 26 US states in 1999, 2003-2004, and 2007-2014 were queried to address this question. Proportionate mortality ratios (PMRs) were calculated to compare specific causes of mortality among HCPs compared with those among the general population. HCPs were less likely to die from heart disease (PMR 93, 95% confidence intervals [CI] 92-94), alcoholism (PMR 62, 95% CI 57-68), drugs (PMR 80, 95% CI 70-90), and more likely to die from cerebrovascular disease (PMR 105, 95% CI 104-107) and diabetes (PMR 107, 95% CI 105-109). HCPs aged 18-64 years were more likely to die by suicide (PMR 104, 95% CI 101-107), whereas those aged 65-90 years were less likely to die by suicide (PMR 84, 95% CI 77-91), with physicians (PMR 251, 95% CI 229-275) and other HCPs having high PMR for suicide. Among all HCPs, suicide PMR was similarly increased, whereas heart disease PMRs are similarly decreased among Black compared with those among White HCPs and those among male compared with those among female HCPs. HCPs as a group and specific types of HCPs demonstrate causes of mortality that differ in important ways from the general population. Race and gender-based trends in PMRs for key causes of mortality among HCPs suggest that employment in a health care field may not alter race and gender disparities noted among the general population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".