Variation of subclinical psychosis as a function of population density across different European settings: Findings from the multi‐national <scp>EU</scp> ‐ <scp>GEI</scp> study
Bibliographic record
Abstract
BACKGROUND: Urbanicity is a well-established risk factor for psychosis. Our recent multi-national study found an association between urbanicity and clinical psychosis in Northern Europe but not in Southern Europe. In this study, we hypothesized that the effect of current urbanicity on variation of schizotypy would be greater in North-western Europe countries than in Southern Europe ones. METHODS: We recruited 1080 individuals representative of the populations aged 18-64 of 14 different sites within 5 countries, classified as either North-western Europe (England, France, and The Netherlands) with Southern Europe (Spain and Italy). Our main outcome was schizotypy, assessed through the Structured Interview for Schizotypy-Revised. Our main exposure was current urbanicity, operationalized as local population density. A priori confounders were age, sex, ethnic minority status, childhood maltreatment, and social capital. Schizotypy variation was assessed using multi-level regression analysis. To test the differential effect of urbanicity between North-western and Southern European, we added an interaction term between population density and region of recruitment. RESULTS: = 6.85; p = 0.009). The effect of urbanicity on schizotypy was substantially stronger in North-western Europe (β = 0.620,95%CI = 0.362-0.877;p < 0.001) compared with Southern Europe (β = 0.190,95%CI = 0.083-0.297;p = 0.001). CONCLUSIONS: The association between urbanicity and both subclinical schizotypy and clinical psychosis, rather than being universal, is context-specific. Considering that urbanization is a rapid and global process, further research is needed to disentangle the specific factors underlying this relationship.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".