Developing Evidence‐Based Interventions for Foster Children: An Example of a Randomized Clinical Trial with Infants and Toddlers
Bibliographic record
Abstract
Children who enter foster care have usually experienced maltreatment as well as disruptions in relationships with primary caregivers. These children are at risk for a host of problematic outcomes. However, there are few evidence‐based interventions that target foster children. This article presents preliminary data testing the effectiveness of an intervention, Attachment and Biobehavioral Catch‐up, to target relationship formation in young children in the foster care system. Children were randomly assigned to the experimental intervention that was designed to enhance regulatory capabilities or to a control intervention. In both conditions, the foster parents received in‐home training for 10 weekly sessions. Post‐intervention measures were collected 1 month following the completion of the training. Outcome measures included children's diurnal production of cortisol (a stress hormone), and parent report of children's problem behaviors. Children in the experimental intervention group had lower cortisol values than children in the control intervention. Also, the experimental intervention parents reported fewer behavior problems for older versus younger foster children. Results provide preliminary evidence of the effectiveness of an intervention that targets children's regulatory capabilities and serve as an example of how interventions can effectively target foster children in the child welfare system.
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 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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".