THE CANADIAN LONGITUDINAL STUDY ON AGING (CLSA): A PLATFORM FOR RESEARCH ON AGING
Bibliographic record
Abstract
Over the next twenty years, the Canadian Longitudinal Study on Aging (CLSA) will generate a wealth of information to contribute to the advancement of the science of aging and policy development. As a study, CLSA objectives are to examine aging as a dynamic life-course process; investigate the inter-relationship among intrinsic and extrinsic factors from mid-life to older age; and capture the transitions and trajectories of aging-related processes. As a platform, CLSA objectives are to provide infrastructure and build capacity for state-of-the-art, interdisciplinary, population-based research and evidence-based decision making to support the nation as it transitions into several decades of rapid population aging. Information on the changing biological, physical, psychological, and social aspects of people’s lives is being collected to understand how, individually and in combination, they influence the maintenance of health and well-being, and the development of disease and disability as people age. The CLSA is one of the most comprehensive studies of its kind undertaken to date. Its large sample, multidisciplinary focus, and longitudinal design provide ongoing research opportunities unprecedented in Canada and internationally. Recruitment of over 50,000 participants is now complete, and baseline data are available to the research community. The objectives of this Symposium are to: 1) Update on study progress and milestones achieved; 2) Report on key methodological aspects of recruitment, sampling, data collection, outcomes ascertainment; 3) Present findings from initial projects using CLSA data; and 4) Give researchers an understanding of the scope and potential of the CLSA as a platform for research on aging.
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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 | 0.047 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.019 | 0.028 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".