Immigrant and ethnic neighbourhood concentration and reduced child developmental vulnerability
Bibliographic record
Abstract
INTRODUCTION: Studies have consistently demonstrated a gradient between median neighbourhood income and child developmental outcomes. By investigating statistical outliers-neighbourhoods with children exhibiting less or more developmental vulnerability than that predicted by median neighbourhood income-there is an opportunity to identify other neighbourhood characteristics that may be enhancing or impeding early childhood development. OBJECTIVE: Testing a variety of neighbourhood factors, including immigrant or ethnic concentration and characteristics of structural disadvantage (proportion of social assistance recipients, homes in need of major repair, residents with high school education only, lone parent families, and residents moving in the last year) we sought to identify factors associated with more or less developmental vulnerability than that predicted by median neighbourhood income, for young children. METHODS: For this cross-sectional study we used validated Early Development Instrument (EDI) data (2003-2013) linked to demographic and socioeconomic Census and Tax Filer data for 98.3% of Canadian neighbourhoods (n=2,023). The purpose of the instrument is to report, at a population-level, children's school readiness. Children's developmental vulnerability was assessed in five domains (physical health and well-being, emotional maturity, social competence, language and cognitive development, and communication and general knowledge) in relation to the 10th percentile from a national normative sample. Levels of children's neighbourhood vulnerability were determined per domain, as percent of children vulnerable at a given domain. Neighbourhoods were grouped into three cohorts, those having lower than predicted, as predicted, or higher than predicted children's vulnerability according to neighbourhood median income. Using multivariable binary logistic regression we modelled the association between select neighbourhood characteristics and neighbourhoods with lower or higher than predicted vulnerability per domain, compared to neighbourhoods with predicted vulnerability. This allowed us to determine neighbourhood characteristics associated with better or worse child developmental outcomes, at a neighbourhood-level, than that predicted by income. RESULTS: In neighbourhoods with less child developmental vulnerability than that predicted by income, high or low immigrant concentration and ethnic homogeneity was associated with less vulnerability in physical (adjusted odds ratio (aOR) 1.66, 95% CI: 1.43, 1.94), social (aOR 1.30, 95% CI: 1.11, 1.51), and communication domains (aOR 1.24, 95% CI: 1.03, 1.47) compared to neighbourhoods with vulnerability concordant with income. Neighbourhood ethnic homogeneity was consistently associated with less developmental vulnerability than predicted by income across all developmental domains. Neighbourhood-level structural disadvantage was strongly associated with child developmental vulnerability beyond that predicted by median neighbourhood income. CONCLUSION: Canadian neighbourhoods demonstrating less child developmental vulnerability than that predicted by income have greater ethnic and ethnic-immigrant homogeneity than neighbourhoods with child developmental vulnerability concordant with income. Neighbourhood social cohesion and cultural identity may be contributing factors. Neighbourhood structural disadvantage is associated with poorer early childhood development, over and above that predicted by neighbourhood income. Neighbourhood-level policy and programming should address income and non-income related barriers to healthy child development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".