Neighborhood Context and Infant Birthweight Among Recent Immigrant Mothers: A Multilevel Analysis
Bibliographic record
Abstract
OBJECTIVES: We compared the influence of the residential environment and maternal country of origin on birthweight and low birthweight of infants born to recent immigrants to urban Ontario. METHODS: We linked delivery records (1993-2000) to an immigration database (1993-1995) and small-area census data (1996). The data were analyzed with cross-classified random-effects models and standard multilevel methods. Higher-level predictors included 4 independent measures of neighborhood context constructed by factor analysis and maternal world regions of origin. RESULTS: Births (N = 22 189) were distributed across 1396 census tracts and 155 countries of origin. The associations between neighborhood indices and birthweight disappeared after we controlled for the maternal country of origin in a cross-classified multilevel model. Significant associations between world regions and birthweight and low birthweight persisted after we controlled for neighborhood context and individual characteristics. CONCLUSIONS: The residential environment has little, if any, influence on birthweight among recent immigrants to Ontario. Country of origin appears to be a much more important factor in low birthweight among children of recent immigrants than current neighborhood. Findings of neighborhood influences among recent immigrants should be interpreted with caution.
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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 | 0.000 |
| 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".