Online Family Problem-solving Treatment for Pediatric Traumatic Brain Injury
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To determine whether online family problem-solving treatment (OFPST) is more effective in improving behavioral outcomes after pediatric traumatic brain injury with increasing time since injury. METHODS: This was an individual participant data meta-analysis of outcome data from 5 randomized controlled trials of OFPST conducted between 2003 and 2016. We included 359 children ages 5 to 18 years who were hospitalized for moderate-to-severe traumatic brain injury 1 to 24 months earlier. Outcomes, assessed pre- and posttreatment, included parent-reported measures of externalizing, internalizing, and executive function behaviors and social competence. RESULTS: Participants included 231 boys and 128 girls with an average age at injury of 13.6 years. Time since injury and age at injury moderated OFPST efficacy. For earlier ages and short time since injury, control participants demonstrated better externalizing problem scores than those receiving OFPST (Cohen’s d = 0.44; P = .008; n = 295), whereas at older ages and longer time since injury, children receiving OFPST had better scores (Cohen’s d = −0.60; P = .002). Children receiving OFPST were rated as having better executive functioning relative to control participants at a later age at injury, with greater effects seen at longer (Cohen’s d = −0.66; P = .009; n = 298) than shorter (Cohen’s d = −0. 28; P = .028) time since injury. CONCLUSIONS: OFPST may be more beneficial for older children and when begun after the initial months postinjury. With these findings, we shed light on the optimal application of family problem-solving treatments within the first 2 years after injury.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".