Relocation and Social Support Among Older Adults in Rural China
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".