Comparison of Health Care Utilization in Different Usual Sources of Care Among Older People With Cardiovascular Disease in China: Evidence From the Study on Global Ageing and Adult Health
Bibliographic record
Abstract
Objectives: To compare the health care utilization in different usual sources of care (USCs) among the elderly population with cardiovascular disease in China. Methods: Cross-sectional data for 3,340 participants aged ≥50 years with cardiovascular disease from Global AGEing and Adult Health (2010)-China were used. Using the inverse probability of treatment weighting on the propensity score with survey weighting, combined with negative binomial regression and logistic regression models, the correlation between USCs and health care utilization was assessed. Results: Patients using primary care facilities as their USC had fewer hospital admissions (IRR = 0.507, 95% CI = 0.413, 0.623) but more unmet health needs (OR = 1.657, 95% CI = 1.108, 2.478) than those using public hospitals. Patients using public clinics as their USC had higher outpatient visits (IRR = 2.188, 95% CI = 1.630, 2.939) than the private clinics’ group. Conclusion: The difference in inpatient care utilization and unmet health care needs between public hospitals and primary care facilities, and the difference in outpatient care utilization between public and private clinics were significant. Using primary care facilities as USCs, particularly public ones, appeared to increase care accessibility, but it still should be strengthened to better address patients’ health care needs.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".