The association between memory, COVID-19 testing, and COVID-19 incidence in middle-aged and older adults: a prospective analysis of the CLSA
Bibliographic record
Abstract
We investigated the association between pre-COVID-19 memory function and (a) receipt of a COVID-19 test and (b) incidence of COVID-19 using the COVID-19 Questionnaire Study (CQS) of the Canadian Longitudinal Study on Aging (CLSA). The CQS included 28,565 middle-aged and older adults. We regressed receipt of a COVID-19 test on participants’ immediate and delayed recall memory scores and re-ran the regression models with COVID-19 incidence as the outcome. All regression models were adjusted for sociodemographic, lifestyle, and health covariates. In the analytical sample (n = 21,930), higher delayed recall memory (better memory) was significantly associated with lower COVID-19 incidence. However, this association was not significant for immediate recall memory. Immediate and delayed recall memory were not associated with receipt of a COVID-19 test. Health policymakers and practitioners may viewmemory status as a potential risk for COVID-19. Memory status may not be a barrier to COVID-19 testing.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".