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Record W2955217487 · doi:10.1186/s40711-019-0096-y

Self-rated health among elders in different outmigration areas—a case study of rural Anhui, China

2019· article· en· W2955217487 on OpenAlexafffund
Weizhen Dong

Bibliographic record

VenueThe Journal of Chinese Sociology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of Waterloo
FundersAnhui Medical UniversityLupina Foundation
KeywordsChinaGeographySocioeconomicsMigrant workersRural areaSociologyDemographyDemographic economicsGender studiesPolitical scienceEconomic growthEconomics

Abstract

fetched live from OpenAlex

China has been a rapid growing economy in recent decades. Part of its economic development engine comes from internal rural-urban migration. The decades-long rural-urban migration is the result of China’s long-lasting uneven development between its urban and rural areas and its income and resource distribution inequality. Rural Anhui is one of the most affected outmigration regions of China. The absence of young and middle-aged villagers changed its natural villages’ demographics. It broke the self-sufficient rural family structure and their traditional lifestyle with no societal infrastructure to replace family support. Meanwhile, it created aging communities—particularly in relatively poorer villages. This study investigates rural elder villagers’ perception of their physical health in the context of rural-urban migration. It explores the reality of the left-behind rural aging population—their real life challenges and regional disparities reflected in their self-rated health status: those who are living in a relatively poorer region (county) tend to have significantly lower self-rated health (SRH) scores than their counterparts in wealthier areas. Women tend to have lower SRH scores than men, and living alone elders tend to perceive their own physical health to be poorer than others. These findings also show that regional economic condition affect individual lives, women are more vulnerable, and healthy personal interaction is an essential element for wellbeing.

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)0.002
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 score0.061
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.015
GPT teacher head0.350
Teacher spread0.335 · 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

Citations21
Published2019
Admission routes2
Has abstractyes

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