Epidemiology of generalized anxiety disorder in Canadian military personnel
Bibliographic record
Abstract
Introduction: This study examined the prevalence, clinical characteristics, help seeking patterns, and military experiences associated with past-year generalized anxiety disorder (GAD) using a representative sample of military personnel. Methods: Data were from the Canadian Community Health Survey–Canadian Forces Supplement ( n = 5,115 Regular Force, n = 3,286 Reserve Force), conducted by Statistics Canada on behalf of the Department of National Defence in 2002. GAD and other mental disorders were assessed using the World Mental Health Composite International Diagnostic Interview. Clinical features of GAD of interest included mean age of onset and episode length, symptoms, degree of impairment and co-occurring disorders, and perceived need for help and help seeking. Multivariate logistic regression models were conducted to examine the sociodemographic, military characteristics, and mental disorders correlated with past-year GAD. Results: Past-year and lifetime prevalence rates of GAD were 1.7% and 4.4%, respectively. The majority of military personnel with past-year GAD reported being severely impaired at work and in their relationships and social life. Those with past-year GAD, relative to those without it, had higher odds of having another mental disorder. Of military personnel with past-year GAD, 72.2% had sought help. Regular Force personnel, relative to reservists, had higher odds of having past-year GAD, as did individuals who witnessed atrocities. Discussion: GAD is modestly prevalent in the Canadian military and is associated with considerable functional impairment. Nevertheless, high rates of help seeking for GAD may speak to the availability, accessibility, and acceptability of mental health care in the Canadian Armed Forces.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".