Analysis of Participation Willingness and Influencing Factors of Rural Mutual Aid-based Elderly Care
Bibliographic record
Abstract
Since the early 21st century, China has entered an aging society, with the degree of population aging continuing to intensify. According to the Seventh National Population Census data released by the National Bureau of Statistics, individuals aged 60 and above accounted for 21.1% of the total population, while those aged 65 and above constituted 15.4%. This demographic shift coincides with significant socio-familial changes. The legacy of the family planning policy has led to sustained fertility decline, resulting in smaller family sizes dominated by nuclear families and a rise in the prevalence of the "4-2-1" family structure (four grandparents, two parents, one child). Concurrently, increased social mobility, particularly the migration of younger labor from less developed regions to first-tier and mega-cities, has resulted in prolonged physical separation between generations. Consequently, the traditional family-based system for providing elderly care is increasingly inadequate, leading to deficits in daily living assistance, emotional support, and care during illness for older adults. Faced with this deepening population aging and the marked decline in the capacity for traditional familial eldercare, there is an urgent need for greater societal involvement in supporting the elderly population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".