Association between perceived security of the neighbourhood and small‐for‐gestational‐age birth
Bibliographic record
Abstract
Evidence points to an association between a mother's place of residence and her newborn's health, independent of individual characteristics. Neighbourhood constructs such as immigrant density, deprivation and crime have all been separately associated with birth outcomes. Little research has considered the joint influence of variables representing a spectrum of neighbourhood constructs. Subjective vs. objective measures of neighbourhood constructs (e.g. reported vs. perceived crime) are often not considered. We sought to evaluate the relationship between neighbourhood measures of reported crime, neighbourhood perceived security, immigrant density, material/social deprivation, residential stability and the odds of small-for-gestational-age (SGA) birth in an urban setting in Canada. Neighbourhood was defined as police districts (n = 49). We linked Montreal livebirths 1997-2001 (n = 98 330) to police district crime measures, survey data on perceived security, and 2001 census data. We used multi-level analysis to calculate odds ratios (OR) for neighbourhood effects on SGA birth accounting for individual characteristics. Mothers residing in neighbourhoods with the most favourable perception had a lower odds of SGA birth than neighbourhoods with the least favourable perception [OR 0.87, 95% CI 0.77, 0.97]. Mothers in neighbourhoods with lower proportions of immigrants had lower odds of SGA birth relative to neighbourhoods with the highest proportion of immigrants. Reported crime, residential stability and material/social deprivation (accounting for neighbourhood perception) were not associated with SGA birth. Immigrant density and subjective perceptions of neighbourhood security are associated with SGA birth. Public health strategies to improve fetal growth should target neighbourhoods with low perceived security and high immigrant density.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".