Disadvantaged neighborhoods, birth weight, and problem behavior in five- and six-year-old pre-school children: Evidence from a cohort born in Amsterdam
Bibliographic record
Abstract
RATIONALE: Low birth weight has been found to increase the problem behavior of children. Yet, little attention has been given to adequately account for the impact of the child's neighborhood on this relation. The residential neighborhood is a choice, based on factors that are usually not observed that may also influence birth weight and problem behavior. OBJECTIVE: Using a model that accounts for such endogeneity of both neighborhood choice and birth weight, we have analyzed behavioral problems in 4210 pre-school children between the ages of 5 and 6, birth weight, and neighborhood status, simultaneously. METHOD: The data used are from the Amsterdam Born Children and their Development (ABCD) cohort for whom a complete prospective record of birth outcomes, pregnancy, socio-demographic characteristics, and indicators of problem behavior are available. Neighborhood data obtained from Statistics Netherlands are merged with the ABCD data file. RESULTS: Our results suggest that ignoring endogeneity attenuates the effect of disadvantaged neighborhoods on both birth weight and problem behavior in pre-school children. Living in a disadvantaged neighborhood decreases the birth weight and increases the probability of problem behavior. Accounting for the endogeneity of neighborhood choice increases the estimated impacts (marginal effects: from -10% to -44% for birth weight and from 3% to 11% for problem behavior). Lower birth weight increases the probability of problem behavior, but it is only significant after adjusting for endogeneity. The coefficients of other factors have the expected associations with problem behavior. CONCLUSIONS: These significant effects of disadvantaged neighborhood on birth weight and problem behavior could inform policies and practices that improve neighborhood development for children born in Amsterdam.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".