Determinants of health care demand in poor, rural China: the case of Gansu Province
Bibliographic record
Abstract
This paper examines the determinants that influence health care demand decisions in rural areas of Gansu province, China. This represents the first effort to identify and quantify the effect of price of care on choice of provider in China, and is the first quantitative examination of this topic focusing on poor rural areas in China. In the three-tier health care system in rural China, we further distinguish the public village clinics and private village clinics using a mixed multinomial logit model. The results show that price and distance play significant roles in choice of health care provider. The price elasticity of demand for outpatients is higher for low-income groups than for high-income groups. When outpatients have particular concerns about provider quality or reputation, or when their health status is poor, distance tends to matter less, i.e. they are willing to travel further in order to obtain better treatment for their illness. Insurance status has a significant impact on the choice of public village clinics relative to self-treatment. Furthermore, age and the attributes of illness are also statistically significant factors. We discuss the policy implications of the results for meeting the health care needs of the poor in rural China.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".