Post-traumatic stress disorder in a Canadian population of medical students, residents, and physicians
Bibliographic record
Abstract
BACKGROUND: Physicians encounter stressors with potential long-term psychological consequences. However, a comprehensive picture of post-traumatic stress disorder (PTSD) prevalence and symptomatic work-related event occurrence across practice stages is lacking. OBJECTIVE: To evaluate PTSD prevalence and the occurrence of work-related symptomatic events among physicians and medical learners. METHODS: In 2017, we surveyed 3,036 physicians, residents, and students within the province of Saskatchewan, Canada. Participants completed the Life Events Checklist (LEC) for DSM 4 and the PTSD Checklist for DSM 4-Civilian version (PCL-C). They also reported work-related events that triggered PTSD-like symptoms. The prevalence of a positive PTSD screen (PCL-C ≥ 36) and the proportion identifying a symptomatic work event were determined. The t-test, Chi-square test, and multiple regression were used to evaluate associations between respondent characteristics and these outcomes. RESULTS: Among 565 respondents, 21.2% screened positively, with similarity across career stages. Thirty-nine percent reported a symptom-inducing work event, with many training-related. Although independent PTSD predictors were not identified, partnered residents and surgical residents were more likely to identify a work-related event. Internationally trained practicing physicians were less likely to identify an event. CONCLUSION: Both symptom-inducing work events and PTSD are frequent, broadly based concerns requiring better preventive strategies across career stages.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".