Income inequality within urban settings and depressive symptoms among adolescents
Bibliographic record
Abstract
BACKGROUND: Although recent evidence has shown that area-level income inequality is related to increased risk for depression among adults, few studies have tested this association among adolescents. METHODS: We analysed the cross-sectional data from a sample of 1878 adolescents living in 38 neighbourhoods participating in the 2008 Boston Youth Survey. Using multilevel linear regression modelling, we: (1) estimated the association between neighbourhood income inequality and depressive symptoms, (2) tested for cross-level interactions between sex and neighbourhood income inequality and (3) examined neighbourhood social cohesion as a mediator of the relationship between income inequality and depressive symptoms. RESULTS: The association between neighbourhood income inequality and depressive symptoms varied significantly by sex, with girls in higher income inequality neighbourhood reporting higher depressive symptom scores, but not boys. Among girls, a unit increase in Gini Z-score was associated with more depressive symptoms (β=0.38, 95% CI 0.28 to 0.47, p=0.01) adjusting for nativity, neighbourhood income, social cohesion, crime and social disorder. There was no evidence that the association between income inequality and depressive symptoms was due to neighbourhood-level differences in social cohesion. CONCLUSIONS: The distribution of incomes within an urban area adversely affects adolescent girls' mental health; future work is needed to understand why, as well as to examine in greater depth the potential consequences of inequality for males, which may have been difficult to detect here.
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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 | 0.033 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; 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".