Early Childbirth Among Foster Youth: A Latent Class Analysis to Determine Subgroups at Increased Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it