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Record W3154307257 · doi:10.2105/ajph.2021.306214

Cumulative Rates of Child Protection Involvement and Terminations of Parental Rights in a California Birth Cohort, 1999–2017

2021· article· en· W3154307257 on OpenAlexaboutno aff
Emily Putnam‐Hornstein, Eunhye Ahn, John Prindle, Joseph Magruder, Daniel Webster, Christopher Wildeman

Bibliographic record

VenueAmerican Journal of Public Health · 2021
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsNeglectFoster careDemographyMedicineCohortChild abuseQuarter (Canadian coin)Injury preventionPoison controlEnvironmental healthPsychiatryGeography

Abstract

fetched live from OpenAlex

Objectives. To document the cumulative childhood risk of different levels of involvement with the child protection system (CPS), including terminations of parental rights (TPRs). Methods. We linked vital records for California’s 1999 birth cohort (n = 519 248) to CPS records from 1999 to 2017. We used sociodemographic information captured at birth to estimate differences in the cumulative percentage of children investigated, substantiated, placed in foster care, and with a TPR. Results. Overall, 26.3% of children were investigated for maltreatment, 10.5% were substantiated, 4.3% were placed in foster care, and 1.1% experienced a TPR. Roughly 1 in 2 Black and Native American children were investigated during childhood. Children receiving public insurance experienced CPS involvement at more than twice the rate of children with private insurance. Conclusions. Findings provide a lower-bound estimate of CPS involvement and extend previous research by documenting demographic differences, including in TPRs. Public Health Implications. Conservatively, CPS investigates more than a quarter of children born in California for abuse or neglect. These data reinforce policy questions about the current scope and reach of our modern CPS.

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)0.001
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 score0.146
Threshold uncertainty score0.516

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.048
GPT teacher head0.352
Teacher spread0.304 · 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

Citations128
Published2021
Admission routes1
Has abstractyes

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