Neighbourhood Income Inequality and General Psychopathology at 3-Years of Age.
Bibliographic record
Abstract
Background: Several studies have linked neighbourhood environment to preschool-aged children's behavioural problems. Income inequality is an identified risk factor for mental health among adolescents, however, little is known as to whether this relationship extends to younger children. Objective: To explore the association between neighbourhood-level income inequality and general psychopathology problems among preschool-aged children. Methods: We analyzed data from the All Our Families (AOF) longitudinal cohort located in Calgary, Canada at 3-years postpartum. The analytical sample consisted of 1615 mother-preschooler dyads nested within 184 neighbourhoods. Mothers completed the National Longitudinal Survey of Children and Youth Child Behaviour Checklist (NLSCY-CBCL), which assessed internalizing and externalizing symptoms. Income inequality was assessed via the Gini coefficient, which quantifies the unequal distribution of income in society. Mixed effects linear regression assessed the relationship between neighbourhood income inequality and preschooler's general psychopathology. Results: The mean Gini coefficient across the 184 neighbourhoods was 0.33 (SD = 0.05; min, max: 0.20-0.56). In the fully adjusted model income inequality was not associated with general psychopathology in children β = 0.07 (95%CI: -0.29, 0.45). Neighbourhood environment accounted for 0.5% of the variance in psychopathology in children. Conclusion: The lack of significant findings may be due to a lack of statistical power in the study. Future studies should investigate this relationship with appropriately powered studies, and over time, to assess if income inequality is a determinant of preschooler psychopathology in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".