Access to residential care in Beijing, China: making the decision to relocate to a residential care facility
Bibliographic record
Abstract
ABSTRACT The demand for residential care by older people is increasing in Beijing as a result of dramatic demographic and socio-economic transformations. Little is known about the way older people access residential care in the context of Beijing. In this research, qualitative data collected from 46 in-depth semi-structured interviews with residential care facility (RCF) managers, older residents, and their family members in six RCFs in Beijing were transcribed and analysed using the constant comparative method. The findings included the following themes: access to residential care as geographical access, information access, economic access, socio-cultural access, and the socio-managerial environment. Geographical access is influenced by location, distance, and the micro-physical environment and amenities of RCFs. Information access refers to the capability to acquire related information on available resources. Economic access is the financial affordability for the resources. Socio-cultural access is affected by individual attitudes and aggregative cultural values on ageing and care of older people. Additionally, the social-managerial environment such as reputations of RCFs, quality of services, and management mechanisms are also important to the decision-making process. All these factors influence older people and their family members’ decision-making process of which RCF to choose. The research provides a multi-perspective analysis of access to residential care and suggestions on improving the accessibility of residential care for older people 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 | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".