Primary Care Visit Rates Among Canadian Veterans in Ontario: A Retrospective Cohort Study of Sex- and Length of Service-stratified Comparisons With Nonveterans
Bibliographic record
Abstract
INTRODUCTION: Research comparing primary care (PC) use among veterans and nonveterans has not widely considered the impact of sex and length of service on the association between veteran status and PC use. We calculated relative differences in the rate of PC visits between Canadian Armed Forces and Royal Canadian Mounted Police veterans and nonveterans overall and by sex and length of service. MATERIALS AND METHODS: We conducted a matched, retrospective cohort study of Canadian veterans and nonveterans residing in Ontario, Canada between 1990 and 2019 using routinely collected linked administrative health care data held at ICES (formerly known as the Institute for Clinical Evaluative Sciences). We compared PC visit rates using multivariable Andersen-Gill (AG) recurrent event regression models. Effect measure modification by sex and length of service was investigated using statistical interaction terms. RESULTS: Overall, veterans had a higher adjusted relative rate (aRR) of PC visits compared to nonveterans (aRR 1.06, 95% CI 1.04-1.07). Male veterans had an aRR of 1.07 (95% CI, 1.05-1.09), while females had an aRR of 1.31 (95% CI, 1.26-1.36). Veterans who served for <5 years had a significantly higher rate of PC visits relative to nonveterans (RR 1.09, 95% CI 1.03-1.15), while veterans who served for ≥30 years had comparable rates to nonveterans (RR 1.00, 95% CI 0.97-1.02). CONCLUSIONS: Veterans had an overall higher rate of PC visits compared to nonveterans, and the effect of veteran status appeared stronger among females and veterans with fewer years of service. The observed differences in rates of PC use could be the result of increased need, increased access to PC, or proactive health care-seeking behaviors retained from military service.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".