Wisdom and Mental Health Across the Lifespan
Bibliographic record
Abstract
OBJECTIVES: The relationships between wisdom and age and between wisdom and mental health are complex with empirical results often inconsistent. We used a lifespan sample and broad, psychometrically sound measures of wisdom and mental health to test for possible age trends in wisdom and its subcomponents and the relationship between wisdom and hedonic and eudaimonic aspects of well-being. METHOD: Participants included 512 Dutch adults ranging in age from 17 to 92 (M age = 46.46, SD = 21.37), including 186 men and 326 women. Participants completed measures of wisdom, physical health, mental health, and personality. RESULTS: Significant quadratic trends indicated that middle-aged adults scored higher on wisdom than younger and older adults. Investigation of wisdom subcomponents illustrated that a complex pattern of increases and decreases in different aspects of wisdom helped account for these age findings. Bivariate correlations showed the expected positive association between wisdom and mental health. Hierarchic regression analyses indicated that the positive association between wisdom and mental health remained significant after accounting for demographic variables (i.e., sex, age, education) and personality traits (i.e., neuroticism, extraversion, and openness to experience). DISCUSSION: Age trends in the components of wisdom (older adults higher in life experience but lower in openness relative to younger and middle-aged adults) help explain the curvilinear pattern showing an advantage in wisdom for middle-aged adults. The greater association between wisdom and eudaimonic well-being suggests that wise persons enhance mental health by pursuing meaningful activities.
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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 | 0.004 |
| 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 | 0.001 |
| 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".