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Record W2987826865 · doi:10.1111/cfs.12704

The functional patterns of adolescent mothers leaving foster care: Results from a cluster analysis

2019· article· en· W2987826865 on OpenAlexaff
Svetlana Shpiegel, Elizabeth M. Aparicio, Bryn King, Dana M. Prince, Jason R. Lynch, Claudette Grinnell‐Davis

Bibliographic record

VenueChild & Family Social Work · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFoster careCluster (spacecraft)PsychologyFoster parentsDevelopmental psychologyMedicineNursing

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.130
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.245
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2019
Admission routes1
Has abstractyes

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