THE CANADIAN LONGITUDINAL STUDY ON AGING: STUDY DESIGN AND METHODS
Bibliographic record
Abstract
The Canadian Longitudinal Study on Aging is following 50,000 men and women aged 45–85, every three years for at least 20 years. Of the total, 20,000 (Tracking participants) are randomly selected within age/sex strata in each province, and 30,000 (Comprehensive participants) are randomly selected within age/sex strata from within 25–50 km of 11 sites across the country (Victoria, Vancouver, Surrey, Calgary, Winnipeg, Ottawa, Hamilton, Montreal, Sherbrooke, Halifax, and St. Johns). Data collection methods include telephone and face-to-face interviews, physical assessments, biological samples, and linkage to administrative databases. Initiated in 2010, the second wave of data collection is currently underway. The CLSA has engaged in a number of “firsts” in Canada. In this presentation we will highlight the study design and content, sampling, recruitment, baseline data collection, and ascertainment of health outcomes. Ethical legal and social issues, as well as accommodation strategies to improve retention in future waves will be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".