First Births to Maltreated Adolescent Girls: Differences Associated With Spending Time in Foster Care
Bibliographic record
Abstract
Few studies have examined early parenting among girls receiving child welfare services (CWS) or disentangled the relationship between maltreatment, spending time in foster care, and adolescent childbirth. Using population-based, linked administrative data, this study calculated birth rates among maltreated adolescent girls and assessed differences in birth rates associated with spending time in foster care. Of the 85,766 girls with substantiated allegations of maltreatment during adolescence, nearly 18% subsequently gave birth. Among girls who spent time in foster care, the proportion was higher (19.5%). Significant variations ( p < .001) were observed in the rate of childbirth across demographic characteristics and maltreatment experiences. When accounting for all of the covariates, spending time in foster care was associated with a modestly higher rate of a first birth (Hazard Ratio [HR] = 1.10; 95% confidence interval = [1.06, 1.14]). While age at first substantiated allegation of maltreatment and race/ethnicity were significant predictors of adolescent childbirth, specific maltreatment experiences were associated with minimal or no differences in birth rates. The findings of this study suggest that the experience of spending time in care may not be a meaningful predictor of giving birth as a teen among CWS-involved adolescent girls and highlight subgroups of this population who may be more vulnerable to early childbirth.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".