The Value of Adaptive Regret Management in Retirement
Bibliographic record
Abstract
This 3-year longitudinal study examined the associations between regret management, everyday activities, and retirement satisfaction among recent retirees. We hypothesized that the regulation of a severe life regret can facilitate activity engagement and retirement satisfaction, but only if retirees manage their regrets adaptively by either increasing effort and commitment when possessing favorable opportunities or disengaging when opportunity is unfavorable. Cross-sectional analyses demonstrated that the highest baseline levels of activity (e.g., volunteering, traveling) and retirement satisfaction were observed among participants who perceived favorable opportunities for addressing their life regrets and had high levels of engagement. Longitudinal analyses showed that this pattern was also associated with increases in activity engagement. In contrast, disengagement protected individuals with unfavorable opportunity from 3-year declines in retirement satisfaction. These findings indicate that adaptive regulation of regrets can both contribute to gains and prevent losses in the early stages of retirement, which may have lasting consequences on retirees' quality of life.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".