Meaning in life across the adult lifespan: age differences in levels and correlates of purpose, significance, and coherence in a broad US sample
Bibliographic record
Abstract
While research into meaning in life (MIL) recognizes its developmental nature, there is little empirical evidence into age differences in MIL, especially in terms of its constituent components purpose, coherence, and significance. Using Local Structural Equation Modeling, we examined cross-sectional mean-level and structural age differences in MIL and its subcomponents in a sample of 782 US adults (19–85 years). We found a general increase in overall MIL, purpose, coherence, and significance across the lifespan. In contrast, exploratory analyses using subjective rather than chronological age showed a decrease from around midlife. Correlations between MIL and its components remained stable across ages, as did correlations with subjective well-being and depressive symptoms. In contrast, overall MIL became more strongly correlated with purpose, coherence, and depressive symptoms with increasing subjective age. These findings contribute fundamentally to a lifespan understanding of MIL, paving the way for future research into how aging experiences shape personal meaning.
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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 | 0.003 |
| 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) | 0.001 | 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".