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Record W2093844805 · doi:10.2190/ag.76.2.a

The Value of Adaptive Regret Management in Retirement

2013· article· en· W2093844805 on OpenAlexafffund
Jamie C. Farquhar, Carsten Wrosch, Dolores Pushkar, Karen Li

Bibliographic record

VenueThe International Journal of Aging and Human Development · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsConcordia University
FundersCanadian Institutes of Health Research
KeywordsRegretDisengagement theoryLife satisfactionLongitudinal studyPsychologyValue (mathematics)Quality of life (healthcare)Baseline (sea)Everyday lifeDemographic economicsSocial psychologyGerontologyEconomicsPolitical scienceMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.242

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.135
GPT teacher head0.392
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2013
Admission routes2
Has abstractyes

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