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
Bibliographic record
Abstract
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.
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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 | 0.052 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| 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".