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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".