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Record W2046528494 · doi:10.1093/geronb/gbu187

Relocation and Social Support Among Older Adults in Rural China

2015· article· en· W2046528494 on OpenAlexaff
Zheng Wu, Margaret J. Penning, Weihong Zeng, Shuzhuo Li, Neena L. Chappell

Bibliographic record

VenueThe Journals of Gerontology Series B · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of Victoria
FundersGordon and Betty Moore Foundation
KeywordsRelocationChinaSocial supportOrdered probitPovertyDemographic economicsEmotional supportPsychologyInstrumental variableSocioeconomicsEconomic growthGeographySocial psychologyEconomics

Abstract

fetched live from OpenAlex

OBJECTIVES: China's economic reforms have led to millions of citizens being relocated to support infrastructure development, reduce poverty, and address ecological, disaster-related and other concerns. This study expands on previous research on the implications of relocation in China by examining the impact of rural elders' relocation on the perceived availability of emotional, instrumental, and financial support. METHODS: Data were drawn from the Ankang Study of Aging and Health conducted with a representative sample of 1,062 rural residents aged 60 and over living in Ankang, China. Two-stage probit and least squares regression models assessed the impact of relocation on familial and nonfamilial emotional, instrumental, and financial support. RESULTS: Relocation was negatively associated with the number of social support resources that older adults perceived as being available. Although this was the case with regard to both familial and nonfamilial support, it was particularly evident with regard to family support and, within families, with regard to instrumental rather than financial or emotional support. DISCUSSION: Relocation has negative implications for the number of social support resources perceived to be available by older adults in rural China. China will need to come to terms with how to provide for the instrumental support needs of an aging society.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.780
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.030
GPT teacher head0.317
Teacher spread0.287 · 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

Citations36
Published2015
Admission routes1
Has abstractyes

Explore more

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