Mental health service use among Canadian Veterans and non-Veterans in Ontario: a population-based study exploring differences among males and females and by length of service
Bibliographic record
Abstract
Veterans of the Canadian Armed Forces (CAF) and Royal Canadian Mounted Police (RCMP) have a higher prevalence of mental health (MH) conditions than members of the Canadian general population (non-Veterans). However, the limited availability of health-related data in Veteran and non-Veteran populations from a common data source, combined with the potential of a healthy worker effect, have historically hindered our ability to make direct comparisons of mental health service use between Veterans and non-Veterans, while the inclusion of small numbers of females in previous research has restricted our ability to explore the effect of sex within Veteran samples. In this dissertation, I used administrative healthcare data from ICES to compare rates of mental health service use (outpatient visits, emergency department visits, and hospitalizations) between Veterans and non-Veterans in Ontario, Canada overall and by sex and length of service.In my first study, I explored the impact of using different matching criteria on the association between Veteran status and an outpatient MH encounter within the first five years following release from the CAF or RCMP, comparing less stringent (hard-matching on age and sex) with more stringent matching criteria (matching on age, sex, region of residence, and median neighbourhood income quintile while restricting to non-Veterans without a history of long term care or rehabilitation stay, or disability or income support payments). I found that, on average, Veterans had a higher rate of outpatient MH visits than non-Veterans, and that the observed effect was stronger among female Veterans. I also found that the intensity of outpatient mental health service use varied over time, with higher rates being observed among Veterans within the first three years compared to years four and five. Last, I found that matching on age, sex, region of residence, and median neighbourhood income quintile resulted in larger relative differences in the effect estimates than the other matching approaches. In my second study, I estimated rates of MH-related emergency department (ED) use using Andersen-Gill recurrent time-to-event models. I found that Veterans had a higher rate of MH-related ED visits compared to non-Veterans, and that the effect was stronger among female Veterans and Veterans with fewer years of service. For my final study, I estimated rates of MH-related hospitalizations within the first ten years following release from the CAF or RCMP. Using a competing risk analysis, where the competing risk was all-cause mortality prior to a MH-related hospitalization, I found that Veterans had a higher rate of MH-related hospitalizations, and that the effect was once again more pronounced among female Veterans and Veterans with fewer years of service. The findings from these studies demonstrate that, on average, Veterans in Ontario, particularly female Veterans and Veterans with fewer years of service, have higher rates of mental health service use than non-Veterans in the years following transition to civilian life.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".