Post-traumatic stress disorder in physicians from an underserviced area
Bibliographic record
Abstract
BACKGROUND: Studies suggest a high prevalence of mental illness in physicians. The rate of post-traumatic stress disorder (PTSD) has been examined in physicians exposed to traumatic circumstances and physicians in training but never in physicians in regular practice. OBJECTIVE: To estimate the prevalence of PTSD in physicians practicing in a predominantly rural and remote and medically underserviced region of Canada. METHODS: The PTSD Checklist-Civilian Version (PCL-C) was mailed to all 331 physicians in Northwestern Ontario, Canada. A PCL-C score of >or=50 was used to define 'probable' PTSD and >or=30 defined 'possible' PTSD. Additional comments and demographic information were also requested. RESULTS: Completed questionnaires were received from 159 physicians (48%). The prevalence of probable PTSD was 4.4%. No differences between demographic groups were observed for probable PTSD, but possible PTSD was more frequent in males than females (47.3% versus 20.4%, chi-square = 10.59, P = 0.001). Mean scores were also higher for males than for females (30.4 versus 25.4, 95% confidence interval for the difference: 1.4-8.5, P = 0.006). Respondents identified overwork, insufficient resources and relationships with colleagues and patients as common stressors. CONCLUSIONS: Results suggest a high rate of PTSD in Northwestern Ontario physicians. The prevalence of possible PTSD and mean PCL-C scores are higher in men than in women in this region, which may relate to differences in practice characteristics and the opportunity for exposure to traumatic events.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".