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Record W2923004281 · doi:10.1177/0956797619831981

Can a Good Life Be Unsatisfying? Within-Person Dynamics of Life Satisfaction and Psychological Well-Being in Late Midlife

2019· article· en· W2923004281 on OpenAlexfundno aff
Henry R. Cowan

Bibliographic record

VenuePsychological Science · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchNorthwestern University
KeywordsPsychologyNeuroticismLife satisfactionExtraversion and introversionPsychological well-beingMultilevel modelWell-beingSet (abstract data type)Subjective well-beingSocial psychologyPersonalityDevelopmental psychologyBig Five personality traitsHappinessPsychotherapist

Abstract

fetched live from OpenAlex

Psychological investigations into the structure of well-being have been largely cross-sectional. However, longitudinal models are needed as Western societies work to improve individual well-being. The current multilevel-modeling study examined within-person dynamics of well-being over 8 years. I asked two questions: (a) How do life satisfaction and psychological well-being (measures drawn from two well-being research traditions) relate over time? and (b) do these relationships vary on the basis of individuals' extraversion or neuroticism? Measures of life satisfaction and psychological well-being were collected in 8 consecutive years from 159 American adults in late midlife. A dispositional-life-satisfaction set point and yearly variation in life satisfaction both related to higher yearly psychological well-being. Neuroticism, but not extraversion, predicted a stronger within-person relationship between life satisfaction and psychological well-being. For participants with very low neuroticism, life satisfaction and psychological well-being varied independently. In sum, experiences of life satisfaction and psychological well-being converged for more neurotic individuals and diverged for more emotionally stable participants.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.331
Teacher spread0.297 · 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.

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

Citations23
Published2019
Admission routes1
Has abstractyes

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