Comparing negative health indicators in male and female veterans with the Canadian general population
Bibliographic record
Abstract
INTRODUCTION: Sex-based information on differences between Canadian veterans and the general population is important to understand veterans' unique health needs and identify areas requiring further research. This study compared various health indicators in male and female veterans with their Canadian counterparts. METHODS: Health indicators for recent-era Regular Force veterans (released between 1998 and 2015) were obtained from the 2016 Life After Service Survey and compared with the general population in the 2015-16 Canadian Community Health Survey using a cross-sectional approach. Age-adjusted rates and 95% CIs were calculated for males and females separately. RESULTS: Compared with Canadians, veterans (both sexes) reported higher prevalence of fair or poor health and mental health, needing help with one or more activity of daily living, lifetime suicidal ideation and being diagnosed with mood and anxiety disorders, post-traumatic stress disorder, migraines, back problems, chronic pain, arthritis, ever having cancer, hearing problems, chronic pain and gastrointestinal problems. A higher prevalence of cardiovascular disease (all types) and high blood pressure was observed in male veterans compared with their Canadian counterparts. Within veterans only, males reported a higher prevalence of diagnosed hearing problems and cardiovascular disease compared with females; conversely females reported a higher prevalence of diagnosed migraines, mood, anxiety and gastrointestinal disorders, and needing help with activities of daily living. These sex differences are similar to the Canadian general population. Some similarities in reporting prevalence between male and female veterans (eg, fair or poor mental health, lifetime suicidal ideation, arthritis, asthma, lifetime cancer incidence, chronic pain and diabetes) were not observed in other Canadians. CONCLUSION: Male and female veterans differed from comparable Canadians, and from each other, in various areas of health. Further research is needed to explore these findings, and veteran-based policies and services should consider sex differences.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.001 | 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) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".