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Record W2899644920 · doi:10.1093/geroni/igy023.2803

NEIGHBORHOOD DANGER AND SLEEP PROBLEMS IN LATER LIFE: THE BUFFERING ROLE OF RELIGION

2018· article· en· W2899644920 on OpenAlexaff
Laura Upenieks

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAttendanceSleep (system call)ResidenceQuality of life (healthcare)GerontologyPsychologyMedicineDemographySociologyPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

Sleep problems are a central consequence of social disadvantages, and residence in a neighborhood with a high degree of danger is often a critical component of this in later life. Religion may be particularly efficacious as a means to improve sleep quality, and religious involvement provides people with companionship and support that might make peaceful sleep more likely, especially for older adults that live in dangerous neighborhoods. This study uses data from the recently released Wave 3 of the National Social Life, Health, and Aging Project (NSHAP, n=3873). Results suggest that neighborhood danger is associated with a greater number of sleep problems, net of demographic and health controls. By contrast, attending religious services once a week or more is associated with fewer sleep problems. Moreover, weekly church attendance buffers the effects of living in a dangerous environment on sleep problems, net of all controls.

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)0.003
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.025
GPT teacher head0.323
Teacher spread0.298 · 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 routes1
Has abstractyes

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