Socioeconomic Differences and Trends in the Place of Death among Elderly People in China
Bibliographic record
Abstract
China is facing a dramatic aging of its population. Little is known about the factors that influence the place of death and the trends in the place of death for elderly people in China. The purposes of this study were: (1) to examine the impact of the socioeconomic status (SES) on place of death for elderly Chinese residents; and (2) to assess temporal trends in the place of death over the last 15 years. Data were derived from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) (1998-2012). Place-of-death as an outcome was dichotomized into either death at home or death outside the home. Logistic regression analyses were used to examine the impact of SES on place of death. The results showed that, of the 23,098 deaths during the study period, 87.78% occurred at home. The overall trend in home death has increased since 2005. SES was shown to be an important factor affecting place of death. The elderly with higher SES were more likely to die where health resources were concentrated, i.e., in a hospital or other type of institution. Our finding suggests that the trend towards a greater emphasis on death at home may call for the development of more supportive home care programs in China. Our finding also suggests that the socioeconomic differences in the place of death may be related to the availability of or access to health care services.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".