Decomposing disparities in the utilization of basic public health services between locals and internal migrants in China: the role of social determinants
Bibliographic record
Abstract
BACKGROUND: Internal migrants in China have long been at a disadvantage in terms of access to publicly financed services, as well as the utilization of public health services. The aim of the study was to examine inequities in the use of basic public health services between internal migrants and the local population and estimate the factors that contributed to inequity in use. METHODS: The data for this study was derived from the 2017 wave of the China Migrants Dynamic Survey. Basic public health services utilization was measured by the establishment of health records, health education and chronic disease management. We performed multivariable logistic regressions to examine inequities in the utilization of basic public health services between locals and internal migrants, and Oaxaca-Blinder decomposition was used to explore possible explanations for such inequities between the two groups. RESULTS: A total of 27,998 cases were included in the analysis. We found that the utilization rates for establishment of health records, health education and chronic disease management among internal migrants were 71.3%, 49.2% and 65.7% lower than their local counterparts, respectively. The decomposition results indicated that the inequities in the establishment of health records between locals and internal migrants were mainly explained by whether people had heard of the National Basic Public Health Services Program (NBPHSP) (17.67%) and by health insurance (5.99%). The contributors to the inequities in health education between locals and internal migrants were community involvement (14.71%) and whether people had heard of the NBPHSP (13.89%). The main factors contributing to the difference in utilization of chronic disease management between the two groups were whether people had heard of the NBPHSP (14.49%) and community involvement (8.43%). CONCLUSIONS: To reduce inequities in the utilization of basic public health services between locals and internal migrants, measures need to be taken to improve knowledge about the basic public health services and to help migrants integrate into the local community.
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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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".