Poverty, Neighbourhood Antisocial Behaviour, and Children’s Mental Health Problems: Findings from the 2014 Ontario Child Health Study
Bibliographic record
Abstract
OBJECTIVES: To determine if levels of neighbourhood poverty and neighbourhood antisocial behaviour modify associations between household poverty and child and youth mental health problems. METHODS: Data come from the 2014 Ontario Child Health Study-a provincially representative survey of 6537 families with 10,802 four- to 17-year-olds. Multivariate multilevel modelling was used to test if neighbourhood poverty and antisocial behaviour interact with household poverty to modify associations with children's externalizing and internalizing problems based on parent assessments of children (4- to 17-year-olds) and self-assessments of youth (12- to 17-year-olds). RESULTS: Based on parent assessments, neighbourhood poverty, and antisocial behaviour modified associations between household poverty and children's mental health problems. Among children living in households below the poverty line, levels of mental health problems were 1) lower when living in neighbourhoods with higher concentrations of poverty and 2) higher when living in neighbourhoods with more antisocial behaviour. These associations were stronger for externalizing versus internalizing problems when conditional on antisocial behaviour and generalized only to youth-assessed externalizing problems. CONCLUSION: The lower levels of externalizing problems reported among children living in poor households in low-income neighbourhoods identify potential challenges with integrating poorer households into more affluent neighbourhoods. More important, children living in poor households located in neighbourhoods exhibiting more antisocial behaviour are at dramatically higher risk for mental health problems. Reducing levels of neighbourhood antisocial behaviour could have large mental health benefits, particularly among poor children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".