Household debt, hypertension and depressive symptoms for older adults
Bibliographic record
Abstract
OBJECTIVES: The Chinese household debt has been increasing rapidly in recent years because of the expansion of consumers' spending and mortgage. Its effects on individuals' mental and physical well-being are poorly known. This study aims to examine the relationship of household debt with hypertension and depressive symptoms among the middle- and old-aged population. METHODS: Nationally representative data were collected from China Health and Retirement Longitudinal Study 2015. Logistic regression analysis and mediation analysis were used to estimate associations of household debt with the presence of hypertension and depressive symptoms. The Sobel test was used to assess the mediation effect of depressive symptoms in the association of household debt and hypertension. RESULTS: Among 12 274 subjects, those with high-level household debt exhibited 12% increased odds of hypertension and double odds of depressive symptoms compared to low-level household debtors. Household debt had a direct effect on hypertension and depressive symptoms and an indirect effect on hypertension via depressive symptoms. CONCLUSIONS: The relationships between household debt, depressive symptoms, and hypertension form a society-psychology-body view that is worth considering in household, community and clinical settings in hypertension management among middle-aged and elderly populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".