Effects of foster care intervention and caregiving quality on the bidirectional development of executive functions and social skills following institutional rearing
Bibliographic record
Abstract
Institutional rearing negatively impacts the development of children's social skills and executive functions (EF). However, little is known about whether childhood social skills mediate the effects of the foster care intervention (FCG) and foster caregiving quality following early institutional rearing on EF and social skills in adolescence. We examined (a) whether children's social skills at 8 years mediate the impact of the FCG on the development of EF at ages 12 and 16 years, and (b) whether social skills and EF at ages 8 and 12 mediate the relation between caregiving quality in foster care at 42 months and subsequent social skills and EF at age 16. Participants included abandoned children from Romanian institutions, who were randomly assigned to a FCG (n = 68) or care as usual (n = 68), and a never-institutionalized group (n = 135). At ages 8, 12, and 16, social skills were assessed via caregiver and teacher reports and EF were assessed via the Cambridge Neuropsychological Test Automated Battery. Caregiving quality of foster caregivers was observed at 42 months. FCG predicted better social skills at 8 years, which in turn predicted better EF in adolescence. Higher caregiver quality in foster care at 42 months predicted better social skills at 8 and 12 years, and better EF at 12 years, which in turn predicted 16-year EF and social skills. These findings suggest that interventions targeting caregiving quality within foster care home environments may have long-lasting positive effects on children's social skills and EF.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".