Post‐Traumatic Stress Disorder in Canada
Bibliographic record
Abstract
Post-traumatic stress disorder (PTSD) has become a global health issue, with prevalence rates ranging from 1.3% to 37.4%. As there is little current data on PTSD in Canada, an epidemiological study was conducted examining PTSD and related comorbid conditions. Modified versions of the Composite International Diagnostic Interview (CIDI) PTSD module, the depression, alcohol and substance abuse sections of the Mini International Neuropsychiatric Interview (MINI), as well as portions of the Childhood Trauma Questionnaire (CTQ) were combined, and administered via telephone interview in English or French. Random digit dialing was used to obtain a nationally representative sample of 2991, aged 18 years and above from across Canada. The prevalence rate of lifetime PTSD in Canada was estimated to be 9.2%, with a rate of current (1-month) PTSD of 2.4%. Traumatic exposure to at least one event sufficient to cause PTSD was reported by 76.1% of respondents. The most common forms of trauma resulting in PTSD included unexpected death of a loved one, sexual assault, and seeing someone badly injured or killed. In respondents meeting criteria for PTSD, the symptoms were chronic in nature, and associated with significant impairment and high rates of comorbidity. PTSD is a common psychiatric disorder in Canada. The results are surprising, given the comparably low rates of violent crime, a small military and few natural disasters. Potential implications of these findings are discussed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".