The trend in primary health care preference in China: a cohort study of 12,508 residents from 2012 to 2018
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Residents' preference for primary health care (PHC) determined their utilization of PHC. This study aimed to assess the determinants of PHC service preference among the residents and the trend in PHC service preference over time in China. METHODS: We employed the nationally representative longitudinal data from 2012 to 2018 based on the China Family Panel Studies. The analysis framework was guided by the Andersen model of health service utilization. We included a total of 12,508 individuals who have been successfully followed up in the surveys of 2012, 2014, 2016, and 2018 without any missing data. Logistic regressions were performed to analyze potential predictors of PHC preference behavior. RESULTS: The results indicated that individuals' socio-economic circumstances and their health status factors were statistically significant determinants of PHC preference. Notably, over time, the residents' likelihood of choosing PHC service represented a decreasing trend. Compare to 2012, the likelihood of PHC service preference decreased by 18.6% (OR, 0.814; 95% CI, 0.764-0.867) in 2014, 30.0% (OR, 0.700; 95% CI, 0.657-0.745) in 2016, and 34.9% (OR, 0.651; 95% CI, 0.611-0.694) in 2018. The decrease was significantly associated with the changes in residents' health status. CONCLUSIONS: The residents' likelihood of choosing PHC service represented a decreasing trend, which was contrary to the objective of China's National Health Reform in 2009. We recommend that policymakers adjust the primary service items in PHC facilities and strengthen the coordination of service between PHC institutions and higher-level hospitals.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it