Multivariate assessment of health-related quality of life in Canadian Armed Forces Veterans after transition to civilian life
Bibliographic record
Abstract
Introduction: The goal of this study was to identify factors associated with the SF-12 Physical Component Summary (PCS) and Mental Component Summary (MCS) measures of health-related quality of life (HRQOL) in former Canadian Armed Forces (CAF) Veterans after transition to civilian life. Methods: Data were taken from the 2010 Survey on Transition to Civilian Life, a national computer-assisted telephone survey of CAF Regular Force personnel who released during 1998–2007. Multivariate linear regression models were developed using a variety of socio-economic, military, health, and disability characteristics. Results: Mean age was 46 years (range 20–67 y), and 12% of the participants were women. Higher age was associated with lower PCS but higher MCS scores. High ratings of mastery and high satisfaction with life were strongly associated with higher scores on both the PCS and the MCS. Most chronic physical health conditions were associated with poorer PCS scores, in particular chronic pain, musculoskeletal conditions, cancer, gastrointestinal conditions, hearing problems and, to a lesser degree, chronic mental health conditions. The only chronic condition associated with poorer MCS scores was presence of one or more mental health conditions. Both activity limitation in major life domains and needing assistance with activities of daily living were negatively associated with PCS scores, whereas only the latter was negatively associated with MCS scores. Discussion: The models suggested protective factors and identified characteristics of subgroups vulnerable to poor HRQOL after accounting for confounding. Findings can be used to identify those at high risk who may benefit from targeted interventions and to develop health promotion and prevention strategies for Canadian Armed Forces personnel in transition to civilian life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".