Do socioeconomic and birth order gradients in child maltreatment differ by immigrant status?
Bibliographic record
Abstract
BACKGROUND: While literature has documented strong gradients in child maltreatment (CM) by socioeconomic status and family composition in the general population, how these patterns extend to immigrants remain inconclusive. Using population-based administrative data, we examined, for the first time, whether gradients in CM by neighbourhood income and childbirth order vary by immigrant status. METHODS: We used linked hospitalisation, emergency department visits, small-area income, birth and death records with an official Canadian immigration database to create a retrospective cohort of all 1 240 874 children born from 2002 to 2012 in Ontario, Canada, followed from 0 to 5 years. We estimated rate ratios of CM among immigrants and non-immigrants using modified Poisson regression. RESULTS: CM rates were 1.6 per 100 children among non-immigrants and 1.0 among immigrants. CM was positively associated with neighbourhood deprivation. The adjusted rate ratio (ARR) of CM in the lowest neighbourhood income quintile versus the highest quintile was 1.57 (95% CI 1.49 to 1.66) for non-immigrants and 1.33 (95% CI 1.15 to 1.54) for immigrants. The socioeconomic gradient disappeared when restricted to children of immigrant mothers arrived at 25+ years and in analyses excluding emergency department visits. Compared to a first child, the ARR of CM for a fourth or higher-order child was 1.75 (95% CI 1.63 to 1.89) among non-immigrants and 0.57 (95% CI 0.44 to 0.74) among immigrants. CONCLUSIONS: Immigrants exhibited lower CM rates than non-immigrants across neighbourhood income quintiles and differences were greatest in more deprived neighbourhoods. The contrasting birth order gradients between immigrants and non-immigrants require further investigation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".