Characterization of occupational, demographic and health determinants in Canadian reservists veterans and the relationship with poor self-rated health
Bibliographic record
Abstract
Abstract Background Self-rated health is an useful indicator of the general health in specific populations and used to propose interventions after service in the military context. However, there is scarce literature about self- rated health (SRH) in the Canadian Veterans of the Reserve Force and its relationship with demographic, health and occupational characteristics of this specific group. The aims of this research were to determine the SRH in Canadian Reserve Force Veterans and to explore the relationship between demographic, military service and health factors by reserve class. Methods Data from the individuals was collected from the Life After Service (LASS) 2013 survey, including Veterans with Reserve Class C (n = 922) and Class A/B (n = 476). Bivariate and multivariate analysis using logistic regression models, were used to assess the association between the demographic characteristics, physical health, mental health, and military service characteristics and the self-rate health by both reserve classes. Results The overall prevalence of poor SRH in Reserve Class C Veterans was 13.1% (CI:11.08–15.4) and for Reserve Class A/B was 6.9% (CI:5.0–9.1). Different degrees of associations were observed during the bivariate analysis and two different models were produced for each reserve class. Veterans of Reserve Class C showed that being single was (OR = 2.76, CI: 1.47–5.16), being 50–59 years old (OR = 4.6, CI: 1.28–17.11), reporting arthritis (OR = 2.49, CI: 1.33–4.67), back problems (OR = 3.02, CI:1.76–5.16), being obese (OR = 1.96, CI: 1.13–3.38), depression (OR = 2.34, CI: 1.28–4.20), anxiety (OR = 4.11, CI: 2.00–8.42), PTSD (OR = 2.1 CI: 0.98–4.47), and being medically released (OR = 4.48, CI: 2.43–8.24) were all associated with higher odds of poor SRH. The Reserve Class A/B model showed that completing high school (OR = 4.30, CI: 1.37–13.81), reporting arthritis (OR = 6.60, CI: 2.15–20.23), diabetes (OR = 11.19, CI: 2.72–46.0), being obese (OR = 3.37, CI: 1.37–8.27), daily smoking (OR = 2.98, CI: 1.05–8.38), having anxiety (OR = 9.8, CI: 3.70–25.75) were associated with higher odds of poor SRH. Conclusions These results suggested that the relationship of poor SRH with demographic, health and military occupation domains varied depending on the class on the Reserve Force Service. Different strengths of association showed different risk compositions for both populations. This can be used to better understand the health and well-being of Veterans of the Reserve Force.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".