Determinants of and socio-economic disparities in self-rated health in China
Bibliographic record
Abstract
BACKGROUND: Self-rated health (SRH) is not only used to measure health status and health inequalities, but also as a strong predictor of morbidity and mortality. The purpose of this study was to: 1) evaluate the factors that account for variations in self-rated health among Chinese citizens; and to 2) explore the process through which socio-economic status may impact self-rated health. METHODS: Data were derived from the Chinese General Social Survey (CGSS) (2013). Determinants of self-rated health were analyzed along four main dimensions: demographic characteristics, socio-economic status, lifestyle, and psychosocial factors. Multivariate odds ratios for good self-rated health were calculated for different variables in order to analyze the determinants. Binary logistic regression analysis was performed to assess the extent to which lifestyle and psychosocial factors explained the association between socio-economic status and self-rated health. RESULTS: About 65% of the survey respondents reported good self-rated health. Women, the elderly, married or single respondents and residents of Western China were less likely to report good self-rated health. Respondents who were engaged in work, had higher household income, reported high social class and higher socio-economic status compared with peers were more likely to report good self-rated health. Normal weight and physically active respondents along with those reporting a happy life, no depression, and good relationships with families and friends were related to good self-rated health. We also found the effect of socio-economic status on self-rated health was partly explained by lifestyle and psychosocial factors. CONCLUSION: The present findings support the notion that both socio-economic status and lifestyle as well as psychosocial factors were related with good self-rated health. The interventions targeting these factors could improve the health status of the population. The depression was the most influential predictor of self-rated health, especially for the women and the elderly. Although lifestyle and psychosocial factors explained partly the the association between socio-economic status and health, the reason why socio-economic difference exists in health must be further explored. What's more, it needs to be further studied why the same determinant has different influence strengths on the health of different groups of people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".