The impact of ethnic status on the health of female migrants: evidence from China
Bibliographic record
Abstract
BACKGROUND: The health of ethnic minority migrant women is a significant public health concern due to their relative vulnerability in comparison to men. However, there exists a paucity of research on the relationship between ethnic status and the health of migrant women in low-to-middle-income countries, such as China. The objectives of this study is to examine the impact of ethnic status on self-reported health and medical history among migrant women in China. METHODS: Data were drawn from the 2017 nationally representative China Migrants Dynamic Survey (CMDS), which represents a cross-sectional study of 72,444 female migrants in households across China. An ordered logistic regression model was used to assess the association between self-reported health and ethnic minority status among female migrants, with results reported as odds ratios. A propensity score matching (PSM) method was employed to address the issue of endogeneity in the regressions arising from potential selectivity bias inherent in migration. RESULTS: Analysis of 72,444 female migrants revealed significant disparities in health outcomes between ethnic minority and Han migrants. The odds of ethnic minority migrants reporting "Healthy" were 0.776 times the odds for Han migrants (OR = 0.776, p < 0.001), indicating lower odds of reporting good health for ethnic minority migrants. For selected health conditions over the past year, the odds of ethnic minority migrants reporting these conditions were 1.119 times the odds for Han migrants (OR = 1.119, p < 0.001), suggesting higher odds of experiencing health issues among ethnic minority migrants. Further stratification by migration distance revealed more pronounced disparities for interprovincial migrants. Among interprovincial migrants, the odds of ethnic minority females reporting "Healthy" were 0.653 times the odds for Han females (OR = 0.653, p < 0.001), indicating a larger health gap compared to interprovincial migrants. When stratified by time since migration, ethnic minority females who migrated 11 or more years ago had 0.738 times the odds of reporting "Healthy" compared to Han females (OR = 0.738, p < 0.001). This suggests that health disparities persist even for long-term migrants. CONCLUSIONS: Compared to Han migrant women in China, we found that ethnic minority migrant women were more likely to report being in poor health and having a health condition in the past year. In addition, interprovincial migrants and ethnic minority females who migrated more than 11 years ago were more likely to report poor health.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".