MétaCan
Menu
Back to cohort
Record W2145935609 · doi:10.5539/gjhs.v6n3p45

Effects of Social Capital on General Health Status

2014· article· en· W2145935609 on OpenAlexvenueno aff
Ayano Yamaguchi

Bibliographic record

VenueGlobal Journal of Health Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsSocial capitalSocioeconomic statusSocial statusHealth equitySocial determinants of healthSocial inequalityContext (archaeology)InequalitySocial reproductionCausality (physics)SociologyPsychologyDemographic economicsEconomicsHealth careEconomic growthSocial sciencePopulationGeographyDemography

Abstract

fetched live from OpenAlex

This paper discusses the concept of social capital as a potential factor in understanding the controversial relationship between income inequality and individual health status, arguing a positive, important role for social capital. Most of the health research literature focuses on individual health status and reveals that social capital increases individual health. However, the difficulty in measuring social capital, together with what may be the nearly impossible task of attributing causality, should relegate the concept to a more theoretical role in health research. Nonetheless, social capital receives academic attention as a potentially important factor in health research. This paper finds that the mixed results of empirical research on income inequality and health status remain a problem in the context of defining a stable relationship between socioeconomic status and health status. Clearly, further research is needed to elaborate on the income inequality and health relationship. In addition, focused, rigorous examination of social capital in a health context is needed before health researchers can comfortably introduce it as a concept of influence or significance.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.011
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.016
GPT teacher head0.384
Teacher spread0.368 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicHealth disparities and outcomesFrench-language works237,207