Mental Disorder, Psychological Distress, and Functional Status in Canadian Military Personnel
Bibliographic record
Abstract
OBJECTIVE: We examined the overlap between mood and anxiety disorders and psychological distress and their associations with functional status in Canadian Armed Forces (CAF) personnel. METHOD: Data on Regular Forces personnel ( N = 6700) were derived from the 2013 Canadian Forces Mental Health Survey, a nationally representative survey of the CAF personnel. Current psychological distress was assessed using the Kessler K10 scale. Past-month mood and anxiety disorders were assessed using the World Health Organization World Mental Health Composite Diagnostic Interview. RESULTS: The prevalence of psychological distress was the same as that of any past-month mood or anxiety disorder (7.1% for each). A total of 3.8% had both distress and past-month mood or anxiety disorder, 3.3% had past-month disorder without psychological distress, while another 3.3% had psychological distress in the absence of a past-month mood or anxiety disorder. After adjusting for age, sex, marital, education, income, language, element, rank, and alcohol use disorder, individuals with both psychological distress and past-month mood and anxiety disorders exhibited the highest levels of disability, days out of role, and work absenteeism relative to those with neither mental disorders nor psychological distress. Relative to individuals with both disorder and distress, those who endured distress in the absence of mental disorder exhibited lower, but meaningful, levels of disability compared with those with neither disorder nor distress. CONCLUSIONS: Disability is most severe among CAF personnel with both distress and past-month mood and anxiety disorders. Nevertheless, distress in the absence of disorder is prevalent and is associated with meaningful levels of disability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.003 | 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".