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Record W2899614208 · doi:10.1093/geroni/igy023.1318

EFFECTS OF WISDOM AND RELIGIOSITY ON SUBJECTIVE WELL-BEING MEDIATED BY MASTERY AND PURPOSE IN LIFE

2018· article· en· W2899614208 on OpenAlexaffabout
Monika Ardelt, Michel Ferrari

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldPsychology
TopicOptimism, Hope, and Well-being
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReligiosityAssociation (psychology)PsychologyCohortYoung adultPurpose in lifeDevelopmental psychologyClinical psychologyDemographyGerontologySocial psychologyMedicineInternal medicineSociologyPsychotherapist

Abstract

fetched live from OpenAlex

Prior research found that the positive association between wisdom and subjective well-being might at least partially be explained by greater mastery and purpose in life. This study tested whether religiosity provides an alternative pathway to well-being and whether the associations are moderated by age cohort and nation. Using cross-sectional data of 111 older adults (age range 62–99 years, M=77.20, SD=8.98) and 100 young adults (age range 21–30 years, M=24.05, SD=2.69) from the US and Canada, multi-group path analysis confirmed that mastery and purpose in life partially mediated the association between wisdom and well-being for all participants. Among older adults, religiosity offered an alternative pathway to well-being, partially through a greater sense of purpose in life. Religiosity was not directly related to well-being among young adults, but mastery and life purpose mediated the association between religiosity and well-being for US young adults, indicating the importance of both age cohort and place.

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)0.006
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.007
GPT teacher head0.267
Teacher spread0.260 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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