Cognitive maintenance in older adults in social classes: a secondary analysis of the longitudinal SHARE data
Bibliographic record
Abstract
BACKGROUND: Cognitive maintenance-defined as a capacity to maintain good or excellent cognitive functioning-is a valuable ageing outcome. Socio-demographic, dementia risk and protective factors may contribute differently to it across social classes. However, these effects have not been adequately assessed yet. OBJECTIVE: This study aims to evaluate the effects of socio-demographic, risks and protective factors on the probability of cognitive maintenance in older adults stratified by social classes. METHODS: Participants aged 65-85 years at the baseline from the Survey on Health, Ageing and Retirement in Europe (Waves 5 (2013) and 7 (2017)) were included. Cognitive maintenance was operationalised as six or more words recalled on the 10-word delayed recall test at baseline and follow-up. Dementia-specific risks and protective factors were selected from global strategies for dementia prevention. Multilevel logistic regressions with the country of residence as a random-effect variable were constructed to compare the relative effect of contributors across social classes. RESULTS: was 0.24, 0.28, 0.41 and 0.32 in participants of higher, middle, lower and not known social classes. Age, number of leisure activities and country of residence were significant predictors for all social classes. Effects of gender, depression, obesity, frailty, alcohol, education, occupation and personality traits vary across social classes. CONCLUSION: Studying contributors to cognitive maintenance separately in social classes may show possible targets of public health strategies for improving cognitive health in populations and reducing social inequalities in cognitive health.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".