Social Stigma and Mental Health among Rural-to-Urban Migrants in China: A Conceptual Framework and Future Research Needs
Bibliographic record
Abstract
There are over 100 million individuals in China who have migrated from rural villages to urban areas for jobs or better lives without permanent urban residency (i.e., "rural-to-urban migrants"). Our preliminary data from ongoing research among rural-to-urban migrants in China suggest that the migrant population is strongly stigmatized. Moreover, it appears that substantial numbers of these migrants experience mental health symptoms (e.g., depression, anxiety, hostility, social isolation). While the population potentially affected is substantial (more than 9% of the entire population or about one-quarter of the rural labour force in mainland China) and our data seem to indicate that the issue is pervasive in this population, there is limited literature on the topic in China or elsewhere. Therefore, in the current article, we utilize secondary data from public resources (i.e., scientific literature, governmental publications, public media) and our own qualitative data to explore the issues of stigmatization and mental health, to propose a conceptual model for studying the association between the stigmatization and mental health among this population, and to identify some future needs of research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".