Provision and Utilisation of Community Care Services in Beijing, China
Bibliographic record
Abstract
“Ageing in place” is the preferred choice for the majority of older persons in China, and community care services support them in living in their homes as long as possible. Based on statistics, interview data, and policy documents, this study analyses the provision and utilisation of community care facilities in Beijing by conducting spatial and qualitative analysis based on structuration theory. The mapping and spatial analysis reveal spatial inequity in the allocation of community care facilities in Beijing, which increased from 494 in 2017 to 1,201 in 2020. Communities in suburban areas, where the older population is growing the fastest, have poorer spatial accessibility to community care services than those located in the central city. Qualitative analysis based on interviews with 28 older residents in six communities, and six managers and staff members of community care facilities, revealed high demands for community care services including daily life support, emotional support, and healthcare services, with low utilisation of the services and many challenges for service provision. Poor perception and evaluation of the services by older persons result in low utilisation of the services. Financial difficulties in service provision and limited support from the government are the major challenges reported by the managers. Strategies have also been developed to meet these challenges by the older persons and managers. Text analysis of policy documents shows the development of regulations and policies on the construction, support, and quality management of the community care system. The complex dynamics of interaction between structure and agency in resource allocation and daily practice of service delivery shapes the provision and utilisation of community care services as a relatively new mode for aged care in China. The findings support understanding the challenges of community care service provision and utilisation from a health geography perspective. Joint efforts from local governments, communities, the private sector, and older residents are needed to support ageing in place 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".