The health of immigrant children who live in areas with high immigrant concentration
Bibliographic record
Abstract
OBJECTIVES: Our objective is to contribute to the literature regarding the association between immigrant children's health, their ethnicity and their living in neighbourhoods with a high ethnic concentration of one's own ethnicity. Using data from families from five ethnic groups who all immigrated to Vancouver metropolitan region in Canada, our research question asks: How ethnicity, ethnic concentration and living in a neighbourhood with others of the same ethnic background contribute to the health of immigrant children? DESIGN: Two data sets are integrated in our study. The first is the New Canadian Children and Youth Study, which collected original data from five ethnic groups who immigrated to metropolitan Vancouver. The second data set, from which we derived neighbourhood data, is the Canadian census. The dependent variable is health status as reported by the parent. Independent variables are at both the individual and neighbourhood levels, including ethnicity, sex and the percentage of people living in the neighbourhood of the same ethnic background. Analysis was completed using hierarchical linear modelling. RESULTS: Children (n = 759) from 24 neighbourhoods were included in the analyses. Health status varied by ethnicity and ethnic concentration, indicating the heterogeneity of immigrant populations. CONCLUSION: With the lack of research on the health of immigrant children and youth living in ethnic concentrations, our findings make an important contribution to understanding the influences on the well-being of immigrant populations.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".