Risks and protective factors for cognitive maintenance in men and women: A secondary analysis of the longitudinal SHARE data
Bibliographic record
Abstract
OBJECTIVE: Maintaining good or excellent cognition is important for the autonomy and quality of life of older adults. Cognitive maintenance in later life results from the combined influence of protective and risk factors, yet broader structural contexts - such as gender, socioeconomic status (SES) and welfare regimes - may also play a crucial role. However, no study has comprehensively assessed how individual and structural factors interact to influence cognitive maintenance in older adults. This study evaluates the relative contributions of sociodemographic factors, dementia risks and protective factors, SES and welfare type to cognitive maintenance in older men and women over a four-year follow-up. STUDY DESIGN: We conducted a secondary analysis of the longitudinal data from waves 5 and 7 (2013-2017) of the Survey on Health, Aging, and Retirement in Europe (SHARE). METHODS: Cognitive maintenance was operationalized as stable good delayed recall performance over four years. A series of multilevel logistic regression models was constructed, with a country of residence included as a random effect. Analyses were stratified by gender and welfare regime to examine contextual differences. RESULTS: Age and SES emerged as the strongest predictors of cognitive maintenance in both genders, with a steeper SES gradient among women. The country of residence was the next most important predictor, while individual risk and protective factors contributed relatively less to the probability of cognitive maintenance. Stratification by welfare type revealed differences in cognitive maintenance prevalence, particularly in corporative and socio-democratic welfare regimes. CONCLUSION: Population-level interventions aimed at reducing social inequalities, promoting inclusion and addressing gender disparities should be central to cognitive health promotion strategies. Further research is needed to identify the active components of different welfare models that support cognitive maintenance, particularly in so-called corporative and socio-democratic countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".