Early- to Late-Life Environmental Factors and Late-Life Global Cognition: The SONIC study
Bibliographic record
Abstract
Abstract Life environment across the life course—such as engagement in late-life leisure activity (LA), primary occupation, and early-life education—have been reported to be associated with better late-life cognitive outcomes. However, few studies have included all these factors from the past to the present due to the time-consuming procedure to measure all factors. This study examined (1) whether late-life LA is associated with better late-life cognition, after considering other life environments and (2) whether occupation, education, and childhood intelligence quotient have indirect effects on the late-life cognition through late-life LA. We used baseline data from the groups of 70- and 80-year-olds in the SONIC study (N = 1721 ). Global cognition was measured using the Montreal Cognitive Assessment. As for LA, participants were asked for yes/no answers to questions regarding their engagement in 158 activities. A latent factor representing LA was used in the analyses. We retrospectively evaluated the complexity of work with data, people, and things. As early-life environments, education and language and arithmetic abilities during elementary school were included in the analyses. Age and gender were controlled. A structural equation model showed that late-life LA was significantly associated with higher global cognition, even after controlling for all past factors (RMSEA = .050, GFI = .973, AGFI = .947). Sobel tests showed significant indirect effects of occupation, education, and childhood abilities on cognitive function. Results were robust across age and gender. It is suggested that engagement in LA explains individual differences in late-life cognitive function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".