Examining the municipal-level representativeness of the Canadian Longitudinal Study on Aging (CLSA) cohort: an analysis using Calgary participant baseline data
Bibliographic record
Abstract
INTRODUCTION: The Canadian Longitudinal Study on Aging (CLSA) is a rich, nationally representative population-based resource that can be used for multiple purposes. Although municipalities may wish to use CLSA data to address local policy needs, how well localized CLSA cohorts reflect municipal populations is unknown. Because Calgary, Alberta, is home to one of 11 CLSA data collection sites, our objective was to explore how well the Calgary CLSA sample represented the general Calgary population on select sociodemographic variables. METHODS: Baseline characteristics (i.e. sex, marital status, ethnicity, education, retirement status, income, immigration, internal migration) of CLSA participants who visited the Calgary data collection site between 2011 and 2015 were compared to analogous profiles derived from the 2011 National Household Survey (NHS) and 2016 Census datasets, which spanned the years when data were collected on the CLSA participants. RESULTS: Calgary CLSA participants were representative of the Calgary population for age, sex and Indigenous identity. Discrepancies of over 5% with the NHS and/or 2016 Census were found for marital status, measures of ethnic diversity (i.e. immigrant status, place of birth, non-official language spoken at home), internal migration, income, retirement status and education. CONCLUSION: Voluntary studies face challenges in recruiting fully representative cohorts. Communities opting to use CLSA data at a municipal level, including the 10 other CLSA data collection sites, should exercise caution when interpreting the results of these analyses, as CLSA participants may not be fully representative of the local population on select characteristics of interest.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| 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".