On the quality of adjustment to retirement: The longitudinal role of personality traits and generativity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it