THE IMPACT OF CHILDHOOD READING ENVIRONMENTS ON COGNITIVE HEALTH IN LATER LIFE AMONG OLDER EUROPEANS
Bibliographic record
Abstract
Abstract While educational achievement helps to accumulate cognitive reserve and promote cognitive function in later life, the influence of the early living environment before completing formal education remains unclear. This study aims to investigate how childhood reading environments, alongside educational achievements, jointly contribute to cognitive health in older adulthood. Analyzing life history data from 123,199 older Europeans aged 50 and above, sourced from the Survey of Health, Ageing and Retirement in Europe, this research examines the interplay between childhood reading environments and educational achievement and their joint effect on cognitive health at older ages. Having more books in the household at age 10 is positively associated with higher cognitive functioning in later life, independent of educational achievements and other childhood socioeconomic status (SES) factors like household size and number of rooms. Additionally, access to books in childhood partially alleviates the cognitive disparities associated with educational achievement. These findings indicate that a rich reading environment at home in childhood offers enduring benefits for cognitive health, which is particularly valuable for individuals who ended up with lower educational achievements. This underscores the potential of interventions focused on enhancing early reading environments to promote long-term cognitive resilience.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".