Low birth weight and neighbourhood of residence: a multi-level analysis
Bibliographic record
Abstract
Low birth weight (LBW) is most often measured at an individual level. However, increasingly, it has been shown that other factors, which are not directly tied to the individual, can influence low birth weight. Specifically, factors such as family income, education level, place of residence, and health care benefits may influence a child's health. Through the database available, I will test the hypothesis that low birth weight is an outcome that can be influenced by the macro-level environment. The objective of this study is to understand the relationship between neighbourhood level factors and LBW in a population of children. It is well known that individual level risk factors influence low birth weight. What is less known is the extent to which a potential determinant of LBW - such as neighbourhood of residence influences low birth weight in a birth cohort in a small prairie city. The main study question is as follows; Do factors related to neighbourhood of residence increase the risk of low birth weight children? The study was comprised of a birth cohort of 5,643 children born in 1992-1994, in Saskatoon, Saskatchewan, Canada. It was found through logistic regression models that the following variables contributed significantly to the prediction of low birth weight; sex (OR=1.75, 95% CI=1.27-2.41), financial assistance (OR=1.5, 95% CI=1.05-2.14), and gestational age (OR=85.8, 95% CI=54.02-136.35). There were also significant interactions between gestational age and parity and gestational age and stillborn births. Neighbourhood characteristics that were related to LBW (unadjusted) were; proportions of residents < grade 9 (p=0.056), dwellings owned (p=0.03), median income (p=0.018), park space (p=0.015), total number of person < $10,000/year (p
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".