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Record W2845304260 · doi:10.1177/1049731518783857

A Comparison of Foster Care Reentry After Adoption in Two Large U.S. States

2018· article· en· W2845304260 on OpenAlexaff
Nancy Rolock, Kevin R. White, Kerrie Ocasio, Lixia Zhang, Michael J. MacKenzie, Rowena Fong

Bibliographic record

VenueResearch on Social Work Practice · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsMcGill University
Fundersnot available
KeywordsReentryFoster careService (business)PsychologyMedicineNursingBusiness

Abstract

fetched live from OpenAlex

Purpose: This study examines foster care reentry after adoption, in Illinois and New Jersey. The provision of services and supports to adoptive families have garnered recent attention due to concern about the long-term stability of adoptive homes. Method: This study used administrative data to examine the pre-adoption characteristics associated with post-adoption foster care reentry. Children were tracked longitudinally, using administrative data, for five to fifteen years (depending on their date of adoption), or the age of majority. Results: Results indicated that most (95%) children did not reenter foster care after adoption. Findings from survival models suggested key covariates that may help to identify children most at risk for post-adoption reentry: child race, age at adoption, number of placement moves in foster care, and time spent in foster care prior to adoption. Conclusion: Study findings may help identify families most at-risk for post-adoption difficulties in order to develop preventative adoption service.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.137
GPT teacher head0.543
Teacher spread0.406 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2018
Admission routes1
Has abstractyes

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