Recovery Trajectories of Child and Family Outcomes Following Online Family Problem-Solving Therapy for Children and Adolescents after Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVES: We conducted joint analyses from five randomized clinical trials (RCTs) of online family problem-solving therapy (OFPST) for children with traumatic brain injury (TBI) to identify child and parent outcomes most sensitive to OFPST and trajectories of recovery over time. METHODS: We examined data from 359 children with complicated mild to severe TBI, aged 5-18, randomized to OFPST or a control condition. Using profile analyses, we examined group differences on parent-reported child (internalizing and externalizing behavior problems, executive function behaviors, social competence) and family outcomes (parental depression, psychological distress, family functioning, parent-child conflict). RESULTS: We found a main effect for measure for both child and family outcomes [F(3, 731) = 7.35, p < .001; F(3, 532) = 4.79, p = .003, respectively], reflecting differing degrees of improvement across measures for both groups. Significant group-by-time interactions indicated that children and families in the OFPST group had fewer problems than controls at both 6 and 18 months post baseline [t(731) = -5.15, p < .001, and t(731) = -3.90, p = .002, respectively, for child outcomes; t(532) = -4.81, p < .001, and t(532) = -3.80, p < .001, respectively, for family outcomes]. CONCLUSIONS: The results suggest limited differences in the measures' responsiveness to treatment while highlighting OFPST's utility in improving both child behavior problems and parent/family functioning. Group differences were greatest at treatment completion and after extended time post treatment.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".