A sex-disaggregated analysis of rank at release and health outcomes among Veterans of the Canadian Armed Forces
Bibliographic record
Abstract
Introduction: Military rank influences individuals' work tasks and experiences and may also relate to their broader life experience. This study examined relationships between military rank at release and various health indicators among male and female Veterans. Methods: Data were obtained from the 2019 Life After Service Survey, a national study of Canadian Veterans released from Canadian Armed Forces Regular Force service between 1998 and 2018. The analytic sample included 2,118 male and 288 female Veterans. Individual logistic regression models were used to calculate odds ratios by rank category and each health variable by sex. Results: For all health outcomes with significant associations, officers fared better than junior and senior non-commissioned members (NCMs). Both senior and junior NCM ranks fared more poorly than officers in self-rated health fair or poor (males and females), chronic obstructive pulmonary disease (males), back problems (males), arthritis (males), stomach ulcer (males), diabetes (males), migraine (males), hearing problems (males), urinary incontinence (females), chronic pain (males and females), self-rated mental health fair or poor (males and females), mood disorder (males), anxiety (males and females), posttraumatic stress disorder (males), and suicidal ideation (males). Significant associations for senior NCM rank only were observed for arthritis (females) and hearing problems (females). A significant association for junior NCM rank only was observed for asthma (males). Discussion: This study highlights the importance of rank and sex in Veteran research and identifies groups at potentially greater risk of negative health outcomes. These findings may inform the development of targeted, sex-specific military health promotion and Veteran services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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) | 0.004 | 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".