Disability correlates in Canadian Armed Forces Regular Force Veterans
Bibliographic record
Abstract
Purpose: This study was undertaken to inform disability mitigation for military veterans by identifying personal, environmental, and health factors associated with activity limitations. Method: A sample of 3154 Canadian Armed Forces Regular Force Veterans who were released during 1998–2007 participated in the 2010 Survey on Transition to Civilian Life. Associations between personal and environmental factors, health conditions and activity limitations were explored using ordinal logistic regression. Results: The prevalence of activity reduction in life domains was higher than the Canadian general population (49% versus 21%), as was needing assistance with at least one activity of daily living (17% versus 5%). Prior to adjusting for health conditions, disability odds were elevated for increased age, females, non-degree post-secondary graduation, low income, junior non-commissioned members, deployment, low social support, low mastery, high life stress, and weak sense of community belonging. Reduced odds were found for private/recruit ranks. Disability odds were highest for chronic pain (10.9), any mental health condition (2.7), and musculoskeletal conditions (2.6), and there was a synergistic additive effect of physical and mental health co-occurrence. Conclusions: Disability, measured as activity limitation, was associated with a range of personal and environmental factors and health conditions, indicating multifactorial and multidisciplinary approaches to disability mitigation.Implications for RehabilitationConsider activity limitations in all veterans with health problems, particularly women or veterans with current or lost marital relationship; post-secondary non-degree education; low income; junior non-commissioned member rank; high life stress; chronically painful conditions; musculoskeletal disorders; or mental health conditions.Comorbidity indicates the need for coordinated multidisciplinary care, especially between physical and mental health care services.Since disability is associated with psychosocial factors, service providers should be aware of the broad range of services and interventions available to mitigate disability in veterans.Do not be led astray by the absence of combat deployment history since disability occurs in former military personnel who have not deployed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".