The trend in primary health care preference in China: a cohort study of 12,508 residents from 2012 to 2018
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".