Examination of Injury, Host, and Social-Environmental Moderators of Online Family Problem Solving Treatment Efficacy for Pediatric Traumatic Brain Injury Using an Individual Participant Data Meta-Analytic Approach
Bibliographic record
Abstract
A series of five randomized controlled clinical trials (RCTs) conducted between 2002 and 2015 support the potential efficacy of online family problem-solving treatment (OFPST) in improving both child and parent/family outcomes after pediatric traumatic brain injury (TBI). However, small sample sizes and heterogeneity across individual studies have precluded examination of potentially important moderators. We jointly analyzed individual participant data (IPD) from these five RCTs, involving 359 children and adolescents between the ages of 5 and 18 years, to confirm the role of previously identified moderators (child's age and pre-treatment symptom levels, parental education) and to examine other potential moderators (race, sex, IQ), using IPD meta-analysis. This reanalysis revealed statistically strong evidence that parental education, child age at baseline, IQ, sex, and parental depression level pre-treatment moderated the effect of OFPST on various outcomes. In particular, children of parents with a less than high school education exhibited fewer internalizing problems and better social competence. Children injured at an older age exhibited fewer externalizing behaviors and less executive dysfunction following OFPST. Child IQ moderated the effect of OFPST on social competence, with significantly better competence for children with lower IQ who received OFPST. Lower levels of parental depression followed OFPST among subgroups with lower IQ, boys, and higher parental depression scores at baseline. Our findings indicate that the optimal application of OFPST is likely to involve older children, those with lower IQ scores, or those from families with lower socioeconomic status (SES).
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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 | 0.036 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.053 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".