Description of a longitudinal cohort to study the health of Canadian Veterans living in Ontario
Bibliographic record
Abstract
Introduction: Social determinants of health are associated with the risk of disease and health services utilization. Understanding the distributions of sex, age, income, and other demographic variables in Canadian Veterans and how they change over time is necessary to optimize service delivery and enhance research validity. This study describes the demographic patterns over time and by age at release in an Ontario cohort of Canadian Armed Forces (CAF) and Royal Canadian Mounted Police (RCMP) Veterans following release. Methods: This is a retrospective cohort study using administrative healthcare data in Ontario from the Institute for Clinical Evaluative Sciences. Veterans were identified using codes housed at the Ministry of Health and Long-Term Care. A descriptive analysis of key demographic variables was presented and stratified by five-year time intervals following release (0–5 years, 5–10 years, 10–15 years, and 15–20 years) and age at release. Results: This cohort includes 23, 818 CAF and RCMP Veterans. At baseline, the average age of the cohort was 41, and 14% were female. Age-specific patterns of median community income and geographic location of residence were noted. In the first five years following release, younger Veterans had a lower income than older Veterans. The majority of older Veterans lived in the Ottawa and Kingston areas following release. Overall, the demographic profile of the cohort was stable over time. Discussion: We have identified a valuable resource to inform the development of relevant provincial public health policy and resource allocation for Veterans. The use of routinely collected healthcare data in Ontario will augment our current understanding of Veteran health in Canada.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".