UNMASKING THE CONSEQUENCES OF THE COVID-19 PANDEMIC ON COGNITION AND 24-HOUR BEHAVIORS: INSIGHTS FROM THE CLSA
Bibliographic record
Abstract
Abstract An unintended side-effect of the COVID-19 pandemic has been changes in lifestyle factors which impact middle-aged and older adult cognition – including changes in 24-hour behaviours (i.e., physical activity, sedentary behaviour, and sleep). In a longitudinal analysis of the Canadian Longitudinal Study on Aging (CLSA) tracking cohort, we explored age- and sex-differences in the effects of the COVID-19 pandemic on cognition and 24-hour behaviours, and whether pandemic-related changes in 24-hour behaviours and cognition are associated. We included cognitively healthy participants at baseline (2012-2105), follow-up 1 (FU1; 2015-2018), and follow-up 2 (FU2; 2018-2021), with complete neuropsychological testing data (N=11,355). Cognition and 24-hour behaviours were indexed at each timepoint. Participants were categorized into pre-pandemic (N=6,174) and post-pandemic (N=5,181) cohorts based on whether FU2 assessments occurred before or after COVID-19 pandemic onset (March 11th, 2020). We examined time x cohort changes in cognition and 24-hour behaviours from FU1 to FU2, and if changes in 24-hour behaviours from FU1 to FU2 were associated with changes in cognition. All models were allowed to vary by age and sex. Our results indicated that post-pandemic cohort males and females aged 65+ years had significantly worse cognition and poorer 24-hour behaviours from FU1 to FU2 than their peers in the pre-pandemic cohort (p’s< 0.05). However, changes in 24-hour behaviours from FU1 to FU2 were unassociated with changes in cognition, irrespective of age or sex. Our results highlight that the pandemic negatively impacted 24-hour behaviours and cognition in older adults – although these effects may be unassociated with each other.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".