Physician suicide demographics and the COVID-19 pandemic
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To identify suicide rates and how they relate to demographic factors (sex, race and ethnicity, age, location) among physicians compared to the general population when aggravated by the coronavirus disease 2019 (COVID-19) pandemic. METHODS: We searched U.S. databases to report global suicide rates and proportionate mortality ratios (PMRs) among U.S. physicians (and non-physicians in health occupations) using National Occupational Mortality Surveillance (NOMS) data and using Wide-ranging Online Data for Epidemiologic Research (WONDER) in the general population. We also reviewed the effects of age, suicide methods and locations, COVID-19 considerations, and potential solutions to current challenges. RESULTS: Between NOMS1 (1985-1998) and NOMS2 (1999-2013), the PMRs for suicide increased in White male physicians (1.77 to 2.03) and Black male physicians (2.50 to 4.24) but decreased in White female physicians (2.66 to 2.42). CONCLUSIONS: The interaction of non-modifiable risk factors, such as sex, race and ethnicity, age, education level/healthcare career, and location, require further investigation. Addressing systemic and organizational problems and personal resilience training are highly recommended, particularly during the additional strain from the COVID-19 pandemic.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it