Regional differences and determinants of self-rated health in a lower middle income rural Society of China
Bibliographic record
Abstract
OBJECTIVE: Self-rated health represents a reliable and important health measure related to general health and quality of life. This study aimed to identify the differences of health states of rural residents in a lower middle income setting in China and its associated factors. METHODS: A descriptive study of a stratified random sample of 3870 individuals was conducted in rural Anhui during 2015. We investigated the influence of five independent variables: individual demographic characteristics, family factors, social capital traits, physical health conditions and healthy lifestyle habits of participants who self-related their health as good. A chi-square test and ordinal logistic regression analyses were used to identify the relationship of these variables and self-rated health. RESULTS: The study found that respondents who negatively rated their health often were female, elderly, poor, lived alone, had low levels of education, inadequate social support, poor physical health, used healthcare services and lived in the lower economic regions. We found no significant correlations between self-rated health and employment, marital status, medical insurance, or exercise frequency. Surprisingly, smoking and drinking also seemed to be unrelated to poor self-reported health. CONCLUSION: Health differences based on region were apparent in rural China. We highlighted the possible impacts of income, age, physical health, education, advanced age, and social support on health. The results from this study could inform the delivery of appropriate health and social healthcare interventions to promote rural residents' health and quality of life.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".