The relationship between internal migration and the likelihood of high-risk pregnancy: Hukou system and high-risk pregnancies in China
Bibliographic record
Abstract
BACKGROUND: China has one of the world's largest internal migrant populations. The Chinese Hukou system is a unique household registration system that limits internal migrants in their access to basic urban public services, such as public health insurance and social assistance of their host city. In the case of female internal migrants, this may lead to high-risk pregnancies. The objective of this study is to assess the relationship between internal migrant status (Hukou) and the likelihood of high-risk pregnancies that occur in one large municipal-level obstetrics hospital in Shanghai, China. METHODS: Medical records data from the Shanghai First Maternity and Infant Hospital from January 1, 2013, to May 31, 2018, were used to analyze 133,358 live births for Shanghai natives (n = 83,872) and internal migrant women (n = 49,486). A propensity score matching approach was used in conjunction with logistic regression analysis to identify the role of internal migrant status (Hukou) on the likelihood of high-risk pregnancies. RESULTS: A greater likelihood of high-risk pregnancies were found among internal migrant women who moved from other parts of China to Shanghai. This effect was more obvious for women who gave birth for the first time and internal migrant women who were employed. CONCLUSION: The results show the effects of internal migrant status (Hukou) and the elevated likelihood of high-risk pregnancies among internal migrant women relative to their urban counterparts in Shanghai even after accounting for self-selection by employing the propensity score matching method. China's unique Hukou household registration system limits access to public services for internal migrant women and accordingly may account for the elevated likelihood of high-risk pregnancies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".