Mental Health Symptoms among Rural-to-Urban Migrants in China: A Comparison with Their Urban and Rural Counterparts
Bibliographic record
Abstract
OBJECTIVE: To examine the mental health symptoms among rural-to-urban migrants in China, in comparison with representative samples of their counterparts in the rural areas from where they emigrated and urban communities to which they immigrated. METHODS: A cross-sectional survey conducted in 2004-2005 in China. Both rural-to-urban migrants (n=1006) and urban residents (n=1000) were recruited in Beijing; the rural resident sample (n=1020) was recruited from the eight provinces of origin for 75% of the migrant sample. Mental health symptoms were measured using the Symptom Checklist-90 (SCL-90). RESULTS: Both rural-to-urban migrants and rural residents scored higher than urban residents in all the SCL-90 global indices and subscales. The rural-to-urban migrants scored higher than rural residents on the SCL-90 Positive Symptom Distress Index and two subscales (depression and psychoticism). The difference remained significant after controlling for a number of key individual characteristics (age, gender, marital status, education, income and perceived general health) in the multivariate model. CONCLUSIONS: The data in the current study demonstrate that rural-to-urban migrants suffer from lower mental health status than both urban residents in the immigrating communities and their rural counterparts in the emigrating communities. The data suggest a possible deteriorative effect of migratory experience on mental health status among rural-to-urban migrants in China and suggest an urgent need for etiological studies and for mental health promotion and prevention efforts among this growing population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".