Health of China's rural-urban migrants and their families: a review of literature from 2000 to 2012
Bibliographic record
Abstract
BACKGROUND: Socioeconomic transformation in China at the beginning of the twenty-first century has led to rapid urbanization and accelerated rural-urban migration. As a result, the concerns about public health problems triggered by increasing internal population mobility have been more widely studied in recent years. SOURCES OF DATA: Published data in Chinese and English on health of migrants and their families in mainland China from 2000 to 2012. AREAS OF AGREEMENT: The shifting patterns of disease distribution due to rural-urban migration, health equity and health reform strategies that cater for this specific yet substantial subpopulation are outstanding concerns. Infectious diseases, mental health, occupational health and women's health are emerging public health priorities related to migration. AREAS OF CONTROVERSY: The high mobility and large numbers of Chinese rural-urban migrants pose challenges to research methods and the reliability of evidence gained. GROWING POINTS: While the theme of working migrants is common in the literature, there have also been some studies of health of those left behind but who often remain unregistered. Migration within China is not a single entity and understanding the dynamics of new and emerging societies will need further study. AREAS TIMELY FOR DEVELOPING RESEARCH: Social, economic, emotional, environmental and behavioural risk factors that impact on health of migrants and their families call for more attention from health policy-makers and researchers in contemporary China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".