Intergenerational differences in social support for the community‐living elderly in Beijing, China
Bibliographic record
Abstract
BACKGROUND AND AIMS: The combination of the rapid process of social-economic development, urbanization, and population ageing brings many challenges for care providers and quality of life of the community-living elderly in Beijing, China. This research aims to understand the intergenerational differences of social support for the elderly in the socio-cultural context of Beijing. METHODS AND RESULTS: To answer this research question, we collected 30 semi-structured in-depth interviews from elders aged 60 and over in three communities in Beijing. The constant comparative method was used for analysis. The results show that the young-old (people aged 60 to 74) received more formal social support and less informal social support compared to their parents' generation. The formal social support they received was not much different but they received less informal social support compared to the older-old (people aged 75 and over) living in the same communities. The young-old expect to receive more formal social support when they become the older-old, as the informal social support from their children would be reduced due to the one-child policy and socio-cultural changes. CONCLUSIONS: Intergenerational differences of social support for the elderly do exist in the form of instrumental, financial, and emotional support. The findings help us understand how socio-economic development and urbanization processes affect the daily life and social support of the community-living elderly from different age groups, and also provides knowledge for improving the quality of life for the elderly in Beijing.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".