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Record W2936064808 · doi:10.1037/pag0000343

Engagement with six major life domains during the transition to retirement: Stability and change for better or worse.

2019· article· en· W2936064808 on OpenAlexfundno aff
Jeremy M. Hamm, Jutta Heckhausen, Jacob Shane, Frank J. Infurna, Margie E. Lachman

Bibliographic record

VenuePsychology and Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsnot available
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchNational Institute on AgingJohn D. and Catherine T. MacArthur Foundation
KeywordsPsycINFOPsychologyDevelopmental psychologyAdult developmentQuality of life (healthcare)Well-beingWork engagementGerontologyMEDLINEWork (physics)Medicine

Abstract

fetched live from OpenAlex

= 6.96, 56% female) to identify profiles of cross-domain engagement and to assess stability and change in these profiles during the transition to retirement. We also examined whether stability and change in the engagement profiles had implications for psychological adjustment. Results of latent profile analyses showed that three profiles of cross-domain engagement emerged both before and after retirement (high engagement, low work engagement, moderate engagement). Latent transition analyses indicated that most participants remained in their preretirement profiles at postretirement, with the majority classified in a profile defined by stable high engagement with multiple life domains. Results of ANCOVAs showed this stable high engagement profile was associated with the most adaptive 9-year changes in cross-domain perceived control, cross-domain situation quality, and cross-dimension eudaimonic well-being. Findings advance the literature by showing that cross-domain profiles of engagement can be identified and that stability and change in these profiles have consequences for longitudinal psychological adjustment in retirement. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.195
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.186
GPT teacher head0.412
Teacher spread0.226 · 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

Citations32
Published2019
Admission routes1
Has abstractyes

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