Sex-specific differences in physical health and health services use among Canadian Veterans: a retrospective cohort study using healthcare administrative data
Bibliographic record
Abstract
INTRODUCTION: Military occupations have historically been, and continue to be, male dominated. As such, female military Veteran populations tend to be understudied, and comparisons of the physical health status and patterns of health services use between male and female Veterans are limited outside of US samples. This study aimed to compare the physical health and health services use between male and female Veterans residing in Ontario, Canada. METHODS: A retrospective cohort of 27 058 male and 4701 female Veterans residing in Ontario whose military service ended between 1990 and 2019 was identified using routinely collected administrative healthcare data. Logistic and Poisson regression models were used to assess sex-specific differences in the prevalence of select physical health conditions and rates of health services use, after multivariable adjustment for age, region of residence, rurality, neighbourhood median income quintile, length of service in years and number of comorbidities. RESULTS: The risk of rheumatoid arthritis and asthma was higher for female Veterans compared with male Veterans. Female Veterans had a lower risk of myocardial infarction, hypertension and diabetes. No sex-specific differences were noted for chronic obstructive pulmonary disease. Female Veterans were also more likely to access all types of health services than male Veterans. Further, female Veterans accessed primary, specialist and emergency department care at greater rates than male Veterans. No significant differences were found in the sex-specific rates of hospitalisations or home care use. CONCLUSIONS: Female Veterans residing in Ontario, Canada have different chronic health risks and engage in health services use more frequently than their male counterparts. These findings have important healthcare policy and programme planning implications, in order to ensure female Veterans have access to appropriate health services.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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, 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".