Mental health service use among Canadian veterans within the first 5 years following service: methodological considerations for comparisons with the general population
Bibliographic record
Abstract
INTRODUCTION: Previous research comparing veteran and civilian mental health (MH) outcomes often assumes stable rates of MH service use over time and relies on standardisation or restriction to adjust for differences in baseline characteristics. We aimed to explore the stability of MH service use in the first 5 years following release from the Canadian Armed Forces and the Royal Canadian Mounted Police, and to demonstrate the impact of using increasingly stringent matching criteria on effect estimates when comparing veterans with civilians, using incident outpatient MH encounters as an example. METHODS: We used administrative healthcare data from veterans and civilians residing in Ontario, Canada to create three hard-matched civilian cohorts: (1) age and sex; (2) age, sex and region of residence; and (3) age, sex, region of residence and median neighbourhood income quintile, while excluding civilians with a history of long-term care or rehabilitation stay or receipt of disability/income support payments. Extended Cox models were used to estimate time-dependent HRs. RESULTS: Across all cohorts, time-dependent analyses suggested that veterans had a significantly higher hazard of an outpatient MH encounter within the first 3 years of follow-up than civilians, but differences were attenuated in years 4-5. More stringent matching decreased baseline differences in unmatched variables and shifted the effect estimates, while sex-stratified analyses revealed stronger effects among women compared with men. CONCLUSIONS: This methods-focused study demonstrates the implications of several study design decisions that should be considered when conducting comparative veteran and civilian health research.
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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 | 0.153 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.006 | 0.003 |
| 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".