The contribution of alternative types of care on life satisfaction among disabled older persons in China
Bibliographic record
Abstract
Abstract Long-term care given to disabled older adults takes many forms, with each impacting life satisfaction through different ways. Drawing data from the 2011–2018 China Health and Retirement Longitudinal Survey, this article explores the effects of various care types on life satisfaction, with a particular focus on disabled older persons. Estimates derived from a fixed effects model with propensity score matching show that compared with formal care, informal care has significant positive effects on life satisfaction for disabled older adults. In addition, informal care has its greatest positive effect on life satisfaction on those who are mildly disabled, men and rural residents compared to their counterparts, while formal care addresses the needs of individuals with severe disability. We find that the main channels of effect occur through reduced loneliness and unhappiness, increased participation in social activities and improved physical health. This work contributes to the existing literature by demonstrating how various care types affect life satisfaction in China where filial piety, the central pillar of the Confucian ethics, is one of the common shared values among residents. These findings highlight the benefits derived from policies that promote and support the provision of informal care for older individuals. Moreover, there is a pressing need to buttress the formal care provision as a supplement to support severely disabled older adults in China.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".