Impact of ethnic density on adult mental disorders: narrative review
Bibliographic record
Abstract
BACKGROUND: The 'ethnic density hypothesis' is a proposition that members of ethnic minority groups may have better mental health when they live in areas with higher proportions of people of the same ethnicity. Investigations into this hypothesis have resulted in a complex and sometimes disparate literature. AIMS: To systematically identify relevant studies, summarise their findings and discuss potential explanations of the associations found between ethnic density and mental disorders. METHOD: A narrative review of studies published up to January 2011, identified through a systematic search strategy. Studies included have a defined ethnic minority sample; some measure of ethnic density defined at a geographical scale smaller than a nation or a US state; and a measure ascertaining mental health or disorder. RESULTS: A total of 34 papers from 29 data-sets were identified. Protective associations between ethnic density and diagnosis of mental disorders were most consistent in older US ecological studies of admission rates. Among more recent multilevel studies, there was some evidence of ethnic density being protective against depression and anxiety for African American people and Hispanic adults in the USA. However, Hispanic, Asian-American and Canadian 'visible minority' adolescents have higher levels of depression at higher ethnic densities. Studies in the UK showed mixed results, with evidence for protective associations most consistent for psychoses. CONCLUSIONS: The most consistent associations with ethnic density are found for psychoses. Ethnic density may also protect against other mental disorders, but presently, as most studies of ethnic density have limited statistical power, and given the heterogeneity of their study designs, our conclusions can only be tentative.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".