Home Environment as a Predictor of Long-Term Executive Functioning following Early Childhood Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: This study examined the relationship of the home environment to long-term executive functioning (EF) following early childhood traumatic brain injury (TBI). METHODS: Participants (N=134) were drawn from a larger parent study of 3- to 6-year-old children hospitalized for severe TBI (n=16), complicated mild/moderate TBI (n=44), or orthopedic injury (OI; n=74), recruited prospectively at four tertiary care hospitals in the United States and followed for an average of 6.8 years post-injury. Quality of the home environment, caregiver psychological distress, and general family functioning were assessed shortly after injury (i.e., early home) and again at follow-up (i.e., late home). Participants completed several performance-based measures of EF at follow-up. Hierarchical regression analyses examined the early and late home environment measures as predictors of EF, both as main effects and as moderators of group differences. RESULTS: The early and late home environment were inconsistent predictors of long-term EF across groups. Group differences in EF were significant for only the TEA-Ch Walk/Don't Walk subtest, with poorer performance in the severe TBI group. However, several significant interactions suggested that the home environment moderated group differences in EF, particularly after complicated mild/moderate TBI. CONCLUSIONS: The home environment is not a consistent predictor of long-term EF in children with early TBI and OI, but may moderate the effects of TBI on EF. The findings suggest that interventions designed to improve the quality of stimulation in children's home environments might reduce the long-term effects of early childhood TBI on EF. (JINS, 2018, 24, 11-21).
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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".