Cognitive measures in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
OBJECTIVE: We describe the implementation of cognitive measures within the Canadian Longitudinal Study on Aging (CLSA), a nationwide, epidemiological study of aging, and relate CLSA Tracking cohort data (n over 20,000) to previous studies using these measures. METHOD: CLSA participants (aged 45-85, n over 50,000) provided demographic, social, physical/clinical, psychological, economic, and health service utilization information relevant to health and aging through telephone interviews (Tracking cohort, n over 20,000) or in-person (i.e. Comprehensive cohort, n over 30,000) in both official languages (i.e. English, French). Cognitive measures included: the Rey Auditory Verbal Learning Test (RAVLT) - Trial 1 and five-minute delayed recall; Animal Fluency (AF), the Mental Alternation Test (MAT) (both cohorts); Controlled Oral Word Association Test, Stroop Test, Prospective Memory Test, and Choice reaction times (Comprehensive Cohort). RESULTS: Performance on the RAVLT Trial 1 and AF were very similar to comparable groups studied previously; CLSA sample sizes were far larger. Within the CLSA Tracking cohort, main effects of age and language were observed for all cognitive measures except RAVLT delayed recall. Interaction effects (language × age) were observed for AF. CONCLUSION: This preliminary examination of the CLSA Tracking cognitive measures lends support to their use in large studies of aging. The CLSA has the potential to provide the 'best' comparison data for adult Canadians generated to date and may also be applicable more broadly. Future studies examining relations among the psychological, biological, health, lifestyle, and social measures within the CLSA will make unique contributions to understanding aging.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".