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Record W2888029016 · doi:10.1177/2378023118795954

Beyond America: Cross-national Context and the Impact of Religious Versus Secular Organizational Membership on Self-rated Health

2018· article· en· W2888029016 on OpenAlexaff
Laura Upenieks, Steven L. Foy, Andrew Miles

Bibliographic record

VenueSocius Sociological Research for a Dynamic World · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsReligious pluralismContext (archaeology)Survey data collectionReligious organizationSocial psychologySociologyPolitical sciencePsychologyGeographyLaw

Abstract

fetched live from OpenAlex

Studies using data from the United States suggest religious organizational involvement is more beneficial for health than secular organizational involvement. Extending beyond the United States, we assess the relative impacts of religious and secular organizational involvement on self-rated health cross-nationally, accounting for national-level religious context. Analyses of data from 33 predominantly Christian countries from the 2005–2008 World Values Survey reveal that active membership in religious organizations is positively associated with self-rated health. This association’s magnitude is higher than the magnitude of associations between many memberships in secular organizations and health. The positive association between involvement in religious organization and self-rated health is moderated by national levels of religious pluralism such that positive associations are primarily found in nations high in religious diversity. These results replicated in a sample of 21 majority-Christian nations from the 2010–2014 World Values Survey.

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 score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.123
GPT teacher head0.518
Teacher spread0.395 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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