Posttraumatic Stress Disorder and Associated Risk Factors in Canadian Peacekeeping Veterans with Health-Related Disabilities
Bibliographic record
Abstract
OBJECTIVES: This study investigates posttraumatic stress disorder (PTSD) and its associated risk factors in a random, national, Canadian sample of United Nations peacekeeping veterans with service-related disabilities. METHODS: Participants included 1016 male veterans (age < 65 years) who served in the Canadian Forces from 1990 to 1999 and were selected from a larger random sample of 1968 veterans who voluntarily and anonymously completed a general health survey conducted by Veterans Affairs Canada in 1999. Survey instruments included the PTSD Checklist-Military Version (PCL-M), Center for Epidemiological Studies-Depression Scale (CES-D), and questionnaires regarding life events during the past year, current stressors, sociodemographic characteristics, and military history. RESULTS: We found that rates of probable PTSD (PCL-M score > 50) among veterans were 10.92% for veterans deployed once and 14.84% for those deployed more than once. The rates of probable clinical depression (CES-D score > 16) were 30.35% for veterans deployed once and 32.62% for those deployed more than once. We found that, in multivariate analyses, probable PTSD rates and PTSD severity were associated with younger age, single marital status, and deployment frequency. CONCLUSIONS: PTSD is an important health concern in the veteran population. Understanding such risk factors as younger age and unmarried status can help predict morbidity among trauma-exposed veterans.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".