Investigating the association between Veteran status and rate of emergency department visits
Bibliographic record
Abstract
Introduction: Canadian Armed Forces (CAF) Veterans' use of emergency department (ED) services could provide insight into unmet primary health care needs, signal health crises, and support policy and program development. Methods: This was a retrospective, matched cohort study of Ontario CAF and Royal Canadian Mounted Police Veterans and non-Veterans, using linked administrative databases at ICES. Ontario Veterans released between 1990 and 2019 were matched on age, sex, income, and geography to non-Veterans. Crude and adjusted relative ED visit rates were calculated using Andersen-Gill recurrent-event regression models. Effect modification by sex and length of service was investigated. Results: Crude ED visit rates of Veterans and matched non-Veterans were 3.20 (95% confidence interval [CI], 3.18-3.23) and 3.15 (95% CI, 3.13-3.16) per 10 person-years of follow-up time, respectively. The adjusted relative rate (RR) was 0.96 (95% CI, 0.93-0.98). The adjusted RR for male Veterans was significantly lower than that for non-Veterans, whereas the adjusted RR was similar for female Veterans and non-Veterans. Length of service was inversely associated with ED visitation rate. Veterans who served less than 5 years had a significantly higher ED visit rate than non-Veterans (RR = 1.17; 95% CI, 1.09-1.26), whereas Veterans who served for 30 years or more had a significantly lower ED visit rate than non-Veterans (RR = 0.78; 95% CI, 0.74-0.82). Discussion: Understanding what these different patterns mean for the design of health services and programs for female Veterans, and for those serving shorter durations, is needed to ensure relevant and timely support is provided.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".