Determinants of self-rated health among shanghai elders: a cross-sectional study
Bibliographic record
Abstract
As the most populous nation in the world, China has now becoming an emerging ageing society. Shanghai is the first city facing the challenge of ageing demographics. Against this background, a study that employs self-rated health (SRH) assessment system was designed to explore the health status of Shanghai elders, and learn their attitudes toward health issues; as well as to investigate the determinants of SRH among Shanghai elders. Understanding SRH is crucial for finding appropriate solutions that could effectively tackle the increasing eldercare demand. This study adopted a quantitative research strategy. Using a multistage stratified cluster sampling method, we conducted a questionnaire survey in August 2011 in Shanghai, which collected 2001 valid survey responses. SRH assessments were categorized by five levels: very good, fairly good, average, fairly poor, or poor. The respondents’ functional status was evaluated using the Barthel index of activities for daily living. In the data analysis, we used chi-squared test to determine differences in socio-demographic characteristics among various groups. Along with statistics, several logistic regression models were designed to determine the associations between internal influence factors and SRH. Younger age (χ 2 = 27.5, p < 0.05), male sex (χ 2 = 11.5, p < 0.1), and living in the suburbs (χ 2 = 55.1, p < 0.05) were associated with better SRH scores. Higher SRH scores were also linked with health behaviour of the respondents; namely, do not smoke (χ 2 = 18.0, p < 0.1), do not drink (χ 2 = 18.6, p < 0.1), or engage in regular outdoor activities (χ 2 = 69.3, p < 0.05). The respondents with better social support report higher SRH scores than those without. Respondents’ ability to hear (χ 2 = 38.7, p < 0.05), speak (χ 2 = 16.1, p < 0.05) and see (χ 2 = 78.3, p < 0.05) impacted their SRH scores as well. Meanwhile, chronic illness except asthma was a major influence factor in low SRH score. Applying multiple regression models, a series of determinants were analysed to establish the extent to which they contribute to SRH. The impact of these variables on SRH scores were 6.6% from socio-demographic and health risk behaviours, 2.4% from social support, 8.5% from mental health, 20% from physical conditions, and13% from chronic diseases. This is the first study that examines the determinants of SRH among Shanghai elders. Nearly 40% of our study’s respondents reported their health status as “good”. The main determinants of SRH among elders include living condition, health risk behaviour, social support, health status, and the economic status of the neighbourhood.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".