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Record W2142221596 · doi:10.1177/0898264314527478

What Type of Social Support Influences Self-Reported Physical and Mental Health Among Older Women?

2014· article· en· W2142221596 on OpenAlexaff
Sabrina T. Wong, Amery D. Wu, Steven E. Gregorich, Eliseo J. Pérez‐Stable

Bibliographic record

VenueJournal of Aging and Health · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
FundersNational Institute on AgingNational Institute of Mental HealthAgency for Healthcare Research and Quality
KeywordsSocial supportEthnic groupMental healthRace and healthPsychologyRace (biology)Clinical psychologyInclusion (mineral)GerontologyEmotional supportHealth equityMedicinePsychiatryPublic healthSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: We examined which types of social support were associated with older women's self-report of physical and mental health and whether the effects of social support were moderated by race/ethnicity. METHOD: Women completed a health behavior survey that included the Medical Outcomes Study-Short Form-12 (MOS SF-12). Single race/ethnic group regressions examined whether different types of social support were related to health. We also examined Pratt's relative importance measures. RESULTS: Emotional support had the strongest effect on both physical and mental health, explaining the highest amount of variation, except among African Americans. Race/ethnicity moderated the association of informational support for Asian women's reports of their mental health. DISCUSSION: For clinicians, assessing individuals' emotional support is important for maintaining or increasing physical and mental health. Clinicians can also assess Asian women's stress, providing informational support accordingly as too much information could be detrimental to their health. For researchers, the inclusion of emotional support items is the most important.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.291
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.403
Teacher spread0.369 · 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.

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

Citations35
Published2014
Admission routes1
Has abstractyes

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