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Record W2771454837 · doi:10.1080/17439760.2017.1414296

Gratitude across the life span: Age differences and links to subjective well-being

2017· article· en· W2771454837 on OpenAlexaff
William J. Chopik, Nicky J. Newton, Lindsay H. Ryan, Todd B. Kashdan, Aaron Jarden

Bibliographic record

VenueThe Journal of Positive Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsWilfrid Laurier University
FundersNational Institute on AgingJohn Templeton Foundation
KeywordsGratitudePsychologyPerspective (graphical)Life spanDevelopmental psychologyInterpersonal communicationYoung adultAdult developmentInterpersonal relationshipWell-beingSocial psychologyGerontologyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

Gratitude has been described as an adaptive evolutionary mechanism that is relevant to healthy psychological and interpersonal outcomes. Questions remain as to whether the presence and benefits of gratitude are consistent from young adulthood to old age; prior research has yielded mixed evidence. We examined the magnitude and direction of age differences in gratitude in three samples (combined N = 31,206). We also examined whether gratitude was associated with greater/lesser well-being at different periods in the life course. We found that the experience of gratitude was greatest in older adults and least in middle aged and younger adults. Further, we found that the associations between gratitude and subjective well-being remained relatively constant across the lifespan. Findings are discussed from a developmental perspective.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.386
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), 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

Citations150
Published2017
Admission routes1
Has abstractyes

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