Poverty and Early Childhood Outcomes
Bibliographic record
Abstract
BACKGROUND: Children born into poverty face many challenges. Exposure to poverty comes in different forms, and children may also transition into or out of poverty. In this study, we examine the relationships among various outcomes and different levels of poverty (household and/or neighborhood poverty) at different points during a child’s first 5 years. METHODS: We used linkable administrative databases, following 46 589 children born in Manitoba, Canada, between 2000 and 2009 to age 7. Poverty is defined as those receiving welfare and those living in low-income neighborhoods. Four outcomes are measured in the first 5 years (placement in out-of-home care, externalizing mental health diagnosis, asthma diagnosis, and hospitalization for injury), with school readiness assessed between ages 5 and 7. RESULTS: Children born into poverty had greater odds of not being ready for school than children not born into poverty (adjusted odds ratio = 1.54, 1.59, 1.26 for children born in household and neighborhood poverty, household poverty only, and neighborhood poverty only, respectively; all significant at P < .05). Similar patterns were seen across outcomes. For those born into neighborhood poverty, the odds of school readiness were higher only if children moved before age 2. CONCLUSIONS: The level of poverty (household or neighborhood) and its duration modify the relationship between early poverty and childhood outcomes. Covariate adjustment generally weakens but does not eliminate these relationships.
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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 | 0.000 |
| 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 | 0.001 |
| 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".