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Record W4416773135 · doi:10.1002/imhj.70057

Retaining infants and young children who experience transitions in care in longitudinal studies of child health and development: Considerations from the HEALthy Brain and Child Development study

2025· review· en· W4416773135 on OpenAlexaff
Julie Poehlmann, Elizabeth I. Johnson, Pilar N. Ossorio, Keisher Highsmith, Brenda Jones Harden, Mishka Terplan, Pilar M. Sanjuan, Lorraine McKelvey, Claire D. Coles, Barbara H. Chaiyachati, H. Kenneth Walker, Rebecca J. Shlafer, Kaitlyn Pritzl, Chandni Anandha Krishnan, S. C. Averill, Samir Das, Jhonny Santiago Torres Peñafiel, Florence Hilliard, Brian S. Gannon, Wesley K. Thompson

Bibliographic record

VenueInfant Mental Health Journal · 2025
Typereview
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsMontreal Neurological Institute and Hospital
FundersNational Institute of Mental HealthNational Institutes of Health
KeywordsLongitudinal studyChild developmentMental healthWelfareCohort studyCohortChild healthFoster care

Abstract

fetched live from OpenAlex

A transition in care (TIC) is a significant change in the primary adults who provide care for a child, involving a move to informal or formal non-parental care, including kinship and foster care. In this paper, we address three issues: (1) the theoretical and empirical reasons for retaining infants and children who experience TIC in longitudinal studies of child health and development; (2) the import of retaining infants and children who experience TIC in studies focusing on parental substance use; and (3) methodological strategies for following children with TIC. We discuss the HEALthy Brain and Child Development (HBCD) study as an example of how a large prospective longitudinal cohort study can retain children who experience TIC, describing strategies such as: (1) documenting the frequency and contexts of these transitions and their associations with child health, mental health, and neurodevelopment; (2) attending to consent and mandated reporting requirements; (3) being sensitive to state child welfare policies and practices; (4) addressing retention challenges; (5) focusing on issues related to diversity, equity, and inclusion; and (6) establishing methods that document transitions and flexibly follow children as they grow older.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.372
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.411
Teacher spread0.346 · 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.

Study designQualitative
Domainnot available
GenreReview

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

Citations0
Published2025
Admission routes1
Has abstractyes

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