Contribution of mental and physical disorders to disability in military personnel
Bibliographic record
Abstract
Background: Combat operations in Southwest Asia have exposed millions of military personnel to risk of mental disorders and physical injuries, including traumatic brain injury (TBI). The contribution of specific disorders to disability is, however, uncertain. Aims: To estimate the contributions of mental and physical health conditions to disability in military personnel. Methods: The sample consisted of military personnel who participated in the cross-sectional 2013 Canadian Forces Mental Health Survey. Disability was measured using the World Health Organization Disability Assessment. The International Classification of Functioning, Disability, and Health was used to classify participants with moderate/severe disability. Chronic mental disorders and physical conditions were measured by self-reported health professional diagnoses, and their contribution to disability was assessed using logistic regression and resulting population attributable fractions. Results: Data were collected from 6696 military members. The prevalence of moderate/severe disability was 10%. Mental disorders accounted for 27% (95% confidence interval [CI] 23-31%) and physical conditions 62% (95% CI 56-67%) of the burden of disability. Chronic musculoskeletal problems 33% (95% CI 26-39%), back problems 29% (95% CI 23-35%), mood disorders 16% (95% CI 11-19%) and post-traumatic stress disorder (PTSD) 9% (95% CI 5-12%) were the leading contributors to disability. After-effects of TBI accounted for only 3% (95% CI 1-4%) of disability. Mental and physical health interacted broadly, such that those with mental disorders experienced disproportionate disability in the presence of physical conditions. Conclusions: Chronic musculoskeletal conditions, back problems, mood disorders and PTSD are primary areas of focus in prevention and control of disability in military personnel.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".