The functional patterns of adolescent mothers leaving foster care: Results from a cluster analysis
Bibliographic record
Abstract
Abstract Few studies have explored the outcomes of adolescent mothers leaving foster care, especially using person‐oriented methods. The current study employed a cluster analysis to identify unique patterns of functioning among adolescent mothers aged 19 (n = 777). Data from the National Youth in Transition Database and the Adoption and Foster Care Analysis and Reporting System were utilized. Findings revealed five subpopulations characterized by distinct constellations of outcomes at age 19. The largest group (43%) exhibited competent functioning across all the domains studied—its members were connected to school and/or employment and did not experience homelessness, substance abuse referrals, or incarceration during the past 2 years (i.e., “resilient”). A relatively small group (12%) exhibited challenges across all the above‐referenced domains, whereas the remaining groups presented challenges in some domains, but not in others. Follow‐up analyses revealed that adolescent mothers classified as resilient at age 19 had the lowest rates of congregate care placements and the highest rates of nonrelative foster care placements at age 17. Moreover, they had lower placement instability and higher rates of extended foster care as compared with members of the other clusters. Implications for practice, policy, and research are discussed.
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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 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".