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Record W4223963007 · doi:10.1080/10508619.2022.2061152

The Association between Religious Participation and Executive Function in Middle- and Older-aged Adults: A Cross-Sectional Analysis of the Canadian Longitudinal Study on Aging

2022· article· en· W4223963007 on OpenAlexaffabout
Sheri Hosseini, Ashok Chaurasia, Mark Oremus

Bibliographic record

VenueInternational Journal for the Psychology of Religion · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of WaterlooMcMaster University
Fundersnot available
KeywordsNeurocognitiveAssociation (psychology)PsychologyGerontologyLongitudinal studyDemographySocial engagementClinical psychologyCognitionMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

We investigated the association between religious participation and executive function in a national sample of Canadian adults aged 45 to 85 years. Executive function scores were aggregated from six neurocognitive tests. We regressed the aggregate scores onto religious participation and controlled for numerous covariates. The analyses were stratified by age: 45 to 64 years and ≥ 65 years. In comparison to persons who reported never participating in religious activities, persons who reported daily-weekly participation had statistically significantly lower executive function scores; we observed this finding for both age groups. Associations for monthly-yearly religious participation versus never participating were also inverse yet not necessarily statistically significant at the 5% level. The strongest inverse associations were observed in models adjusted for social networks, social support, and social participation. Our findings mesh with recent research and suggest the need to carefully assess the role of religious participation when promoting executive function. Future research warrants employing longitudinal designs to further investigate the association.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
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 head0.074
GPT teacher head0.427
Teacher spread0.353 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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