Early Childbirth Among Foster Youth: A Latent Class Analysis to Determine Subgroups at Increased Risk
Bibliographic record
Abstract
CONTEXT: Research has documented elevated rates of early childbirth among adolescents who have spent time in foster care, and a better understanding is needed of the characteristics of vulnerable individuals and the circumstances of their time in care. METHODS: California birth records for 1999-2010 were probabilistically linked to state child welfare service records spanning the same date range to identify females aged 12-19 who had spent time in foster care and had had a first birth before age 20. Latent class analysis was used to identify subgroups based on age at most recent entry into care, length of this stay and three indicators of placement instability. The probability of a first birth being related to class membership was assessed as a distal outcome, and differences across classes were assessed using chi-square tests. RESULTS: Four distinct classes of foster youth were identified: Later Entry/High Instability (20% of individuals), Later Entry/Low Instability (43%), Earlier Entry/High Instability (12%) and Earlier Entry/Low Instability (25%). The probability of a first childbirth ranged from 31% (class 1) to 15% (class 4); classes 2 and 3 experienced moderate risk (23% and 24%, respectively). Two groups were further characterized by high rates of reentry into care, with 56% of class 1 and 41% of class 3 individuals experiencing more than one episode in care. CONCLUSIONS: Identifiable subgroups of female foster youth are at heightened risk of early childbirth and may benefit from early intervention, enhanced support and access to reliable, ongoing sexual and reproductive health care.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".