Education and successful Aging: A systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
Successful aging refers to a multifaceted concept that includes the physical, cognitive, emotional, and social health of older adults. In recent years, growing research interest has focused on the various factors that contribute to positive aging outcomes. This study examines the relationship between educational level and successful aging in individuals aged 65 and over, using a systematic review and meta-analysis of cohort studies. Electronic databases (PubMed, Scopus, ERIC, and PsycINFO) were searched to identify eligible papers following the PRISMA guidelines. Additionally, reference lists of relevant systematic reviews, meta-analyses, and included studies were reviewed. The methodological quality of the selected studies was appraised through the application of the Newcastle-Ottawa Scale (NOS). Combined estimates were calculated using random-effects models with the REML method in R version 4.4.0. Twenty-eight articles met the eligibility criteria and were included in the review and meta-analysis. Statistical analysis showed that upper secondary education (OR = 1.17, 95% CI = 1.09–1.26), tertiary education (OR = 1.27, 95% CI = 1.03 –1.56), and varied educational levels (OR = 1.11, 95% CI = 1.05–1.18) were significantly associated with successful aging of older adults. Based on the current data, higher educational levels are significantly associated with successful aging in later life.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".