The perception and utilization of community care services by older persons in the new urban areas of Beijing, China: A cross‐sectional study
Bibliographic record
Abstract
Background and Aims: In recent years, Beijing, similar to many other large cities in China, has experienced rapid urbanization in many new areas. These have often been locations of population aging but have not had concomitant development of required community care services for older persons. This study aims to understand the perception and utilization of newly developed community care services by the older persons living in this region for providing knowledge to improve the services. Methods: Applying Andersen's behavior model, this study used binary logistic regression of the factors influencing the perception and utilization of community care services and analyzed data collected by questionnaires to 301 older persons in the new urban areas of Beijing. Results: Education, income, activities of daily living, instrumental activities of daily living, mental health, and economic needs are statistically significant factors affecting older persons' perception of community care services. Age, perception of community care services, income, neighborhood relationships, mental health, and emotional needs are significant factors affecting older persons' utilization of community care services. Conclusion: Demographic characteristics, health status, social support, and care needs were significant factors in the perception and utilization of community care services, after controlling for many variables. Improving older persons' perceptions of community care services is likely to promote their access to the services, which should help meet the challenges of rapid population aging in providing community care services to support aging in place in the new urban areas of Beijing, China.
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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 | 0.000 |
| 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".