Trauma Exposure and Posttraumatic Stress Disorder in the Canadian Military
Bibliographic record
Abstract
OBJECTIVE: To estimate the lifetime prevalence of trauma exposure and posttraumatic stress disorder (PTSD) among a representative, active military sample, and to identify demographic and military variables that modulate rates of trauma exposure as well as PTSD rates and duration. METHOD: A cross-sectional weighted sample of 5155 regular members and 3957 reservists (n = 8441) of the Canadian Armed Forces (CAF) was face-to-face interviewed using a lay-administered structured interview that generates Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, psychiatric diagnoses. RESULTS: Within this sample, 85.6% reported 1 or more trauma exposure, with a median number of 3 or more exposures. Compared with males, females were less likely (P < 0.05) to be exposed to warlike trauma (adjusted odds ratio [AOR] 0.40), disasters (AOR 0.43), assaultive violence (AOR 0.52), and witnessing trauma (AOR 0.75). However, they were more likely to report sexual assault (AOR 7.36). The lifetime prevalence of PTSD was 6.6% and the conditional rate was 7.7%. Both lifetime and conditional PTSD rates were higher among female soldiers, but lower among the reserve forces, both male and female. Finally, the median duration of PTSD was negatively influenced by younger age of onset, but not influenced by whether the event occurred during deployment. CONCLUSIONS: Active members of the CAF report a high degree of trauma exposure but a moderate rate of lifetime PTSD.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".