The Canadian Longitudinal Study on Aging: A Vehicle for Research on Aging in Older Veterans
Bibliographic record
Abstract
INTRODUCTION: Research on the health of older Veterans in Canada is an emerging area. Few population-based studies in Canada have included older Veterans as a specific group of interest. This paper describes a cohort of self-identified Veterans within the Canadian Longitudinal Study on Aging (CLSA). MATERIALS AND METHODS: Using data from the CLSA baseline assessment (2011-2015), we describe sociodemographic and health characteristics along with military-related variables in a cohort of Veterans in Canada. We also estimate the number of Canadian and non-Canadian Veterans living in Canada at the time of the CLSA baseline data collection. RESULTS: We estimate that at the CLSA baseline, there were 718,893 (95% confidence interval [CI], 680,033-757,110) Canadian Veterans and 185,548 (95% CI, 165,713-205,100) non-Canadian Veterans aged 45-85 years living in Canada. Veterans were older and predominantly male compared to non-Veterans in the CLSA. Following age and sex adjustment, the distribution of sociodemographic and health characteristics was similar across all groups. The majority (> 85%) of participants in each comparison group reported self-rated general and mental health as excellent, very good, or good. Following age and sex adjustment, most characteristics across groups remained similar. One exception was mental health, where a greater proportion of Veterans screened positive for depression and anxiety relative to non-Veterans. CONCLUSIONS: Using CLSA baseline data, we estimate the number of older Veterans in Canada and present descriptive data that highlight interesting differences and similarities between Veterans and non-Veterans living in Canada. Canadian and non-Canadian Veterans in the CLSA are presented separately, with the latter group having not been previously studied in Canada. This paper presents a snapshot of a cohort of self-identified Veterans within the CLSA at study baseline and highlights the potential of the CLSA as a vehicle for studying the aging Veteran population in Canada for years to come.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.027 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".