Subjective well-being of the elderly in Xi Cheng District, Beijing.
Bibliographic record
Abstract
BACKGROUND: In 2010 the Beijing Municipal Government promulgated a policy aimed at improving the quality of life and subjective well-being of elderly residents that included a component focused on mental health. AIM: Identify factors associated with subjective well-being in a representative sample of elderly residents of Xi Cheng District in Beijing. METHODS: This cross-sectional study administered a self-completion survey to a stratified random sample of 2342 residents of Xi Cheng District who were 60 to 80 years of age. The level of well-being was assessed using a validated Chinese version of the Memorial University of Newfoundland Scale of Happiness (MUNSH). Detailed socioeconomic variables were obtained using a questionnaire developed by the authors. Social support, anxiety, and depression were assessed using validated Chinese versions of the Social Support Rating Scale (SSRS), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS). RESULTS: Among the 2342 respondents, 1616 (69.0%) had a total MUNSH score of 32 or above, indicating a high level of happiness; 423 (18.1%) has a total SSRS score 32 or below, indicating poor social support; 201 (8.6%) had a total SDS score of 53 or above, indicating significant depression; and 126 (5.3%) had a total SAS score of 50 or above, indicating significant anxiety. In the multivariate regression analysis the self-reported level of depression was the most important factor related to well-being. Anxiety, social support, income level, the quality of family relationships, the ability to self-regulate emotions, and regular exercise were also significantly related to well-being; but gender, marital status, age and educational level were not associated with well-being. CONCLUSION: Among elderly urban residents in Beijing, self-reports of poor subjective well-being are closely associated with self-reports of depressive and anxiety symptoms and also associated with social factors such as social support, income level and family relationships. Prospective studies are needed to identify the causal relationships of these variables and, based on the findings, to develop targeted interventions aimed at improving the quality of life and well-being of elderly community members.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".