Digging deeper in Shanghai: towards a ‘mechanism-rich’ epidemiology
Bibliographic record
Abstract
BACKGROUND: There are very few close-up sociological or anthropological data informing epidemiological and psychiatric research design and/or contributing to our understanding of the relationship between mental health and specific forms of urban life. Furthermore, research on the relationships between urbanicity and mental disorder has paid little attention to the global diversity of urban experience, such as in cities in China, India and Brazil. METHODS: Two innovative methods can be employed to unveil the diversified urban experience of migrants in China, i.e. an ethnography-informed sociological deep surveying instrument and an ecological momentary assessment with a smartphone app. This article introduces the design and pilot survey of these new instruments towards a 'mechanism-rich' epidemiology. RESULTS: The ethnography-informed survey instrument enabled us to include some of the issues from the ethnography and successfully 'dig deeper' into respondents' social experience. The pilot of the smartphone app serves as 'proof of principle' that we can recruit respondents in Shanghai, and that we can receive and use the data. CONCLUSIONS: Both of these pilots have demonstrated good feasibility for studying mobility, urban life and mental health. Our next steps will be to extend the Shanghai sample, to use the app in Sao Paulo and Toronto and then hopefully in India and Africa.
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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 | 0.001 | 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) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".