Mental Health Challenges Raised by Rapid Socioeconomic Transformations in China
Bibliographic record
Abstract
China's rapid economic development has contributed to health improvement, such as increasing life expectancy, reducing communicable diseases, and mortality rate. However, the sustainable social and economic transformations, including industrialization, urbanization, globalization, and informatization, have triggered huge challenges to population health in China, particularly to mental health. This review discussed the mental health problems due to socioeconomic changes such as population, life-style, and environment changes, as well both the economic and disease burden of mental disorders. With awareness of these challenges, the following three possible responses are proposed: identify social and economic impact on mental health based on high-quality qualitative and quantitative analysis; improve mental health awareness and literacy; and enhance mental health-care system and promote implementation research. Lessons from China can be a great reference for other low- and middle-income countries. With efforts overcoming the current and potential challenges on mental health, the Sustainable Development Goals on mental health can be possibly achieved by 2030.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".