Understanding future needs of Canadian veterans.
Bibliographic record
Abstract
BACKGROUND: Planning for the future needs of Canadian veterans requires comprehensive and detailed data on the size of the Canadian veteran population and their health. This article describes current veteran population estimates and examines the health of two eras of veterans compared with the health of Canadians in general. DATA AND METHODS: This study describes the size and age structure of the Canadian veteran population forecasted by Veterans Affairs Canada (VAC). Veteran health was examined for two eras of Regular Force veterans. The health of earlier-era veterans (released between 1954 and 2003) was examined using the 2003 Canadian Community Health Survey. The health of recent-era veterans (released between 1998 and 2012) was examined using the 2013 Life After Service Survey. Health indicators for veterans were compared with the Canadian general population using age- and sex-adjusted rates and confidence intervals. RESULTS: The VAC forecast points to a stable population of about 600,000 veterans for the next decade, but a growing proportion will be older than 70 years old. Regular Force veterans of both eras had a higher prevalence than the Canadian general population of activity limitations and back problems, a lower prevalence of low income, and a similar prevalence of life stress and heavy drinking. Recent-era veterans had a higher prevalence than the Canadian general population of many more indicators-in particular, arthritis, self-rated mental health, depression and anxiety. DISCUSSION: Veterans differed from the Canadian general population in many areas of well-being, and recent-era veterans differed in more areas than earlier-era veterans. These results highlight the need for forecasting and planning, and for policy that is sensitive to these differences and incorporates health status changes as veterans age. Multiple data sources will be required to describe the future health needs of the entire Canadian veteran population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".