On the quality of adjustment to retirement: The longitudinal role of personality traits and generativity
Bibliographic record
Abstract
OBJECTIVE: Although psychological factors have been explored in relation to other life transitions, their influence on retirement adjustment quality has been largely overlooked. This study assessed the contribution of personality traits and generativity before retirement in the prediction of hedonic and eudaimonic well-being at two temporal points after retirement. METHOD: This article analyzes data from the Midlife in the United States (MIDUS) longitudinal sample. Specifically, it uses a subsample of people who were not retired at Time 1, but were 9 years after at Time 2 (n = 548) and 18 years after at Time 3 (n = 351). RESULTS: After controlling both for initial values on hedonic and eudaimonic well-being and for the effects of personal attributes and resources, higher scores on Extraversion at Time 1 significantly predicted hedonic well-being at Time 2, whereas lower scores on Neuroticism and higher scores on generativity at Time 1 significantly predicted eudaimonic well-being at Time 2. Neuroticism and generative concern at Time 1 remained significant in the prediction of eudaimonic well-being at Time 3. CONCLUSIONS: The study shows that personality traits and generative concern at midlife explain a meaningful part of the variation in individuals' quality of subsequent retirement adjustment.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".