Online Problem Solving for Adolescent Brain Injury: A Randomized Trial of 2 Approaches
Bibliographic record
Abstract
OBJECTIVE: Adolescent traumatic brain injury (TBI) contributes to deficits in executive functioning and behavior, but few evidence-based treatments exist. We conducted a randomized clinical trial comparing Teen Online Problem Solving with Family (TOPS-Family) with Teen Online Problem Solving with Teen Only (TOPS-TO) or the access to Internet Resources Comparison (IRC) group. METHODS: Children, aged 11 to 18 years, who sustained a complicated mild-to-severe TBI in the previous 18 months were randomly assigned to the TOPS-Family (49), TOPS-TO (51), or IRC group (52). Parent and self-report measures of externalizing behaviors and executive functioning were completed before treatment and 6 months later. Treatment effects were examined using linear regression models, adjusting for baseline symptom levels. Age, maternal education, and family stresses were examined as moderators. RESULTS: The TOPS-Family group had lower levels of parent-reported executive dysfunction at follow-up than the TOPS-TO group, and differences between the TOPS-Family and IRC groups approached significance. Maternal education moderated improvements in parent-reported externalizing behaviors, with less educated parents in the TOPS-Family group reporting fewer symptoms. On the self-report Behavior Rating Inventory of Executive Functions, treatment efficacy varied with the level of parental stresses. The TOPS-Family group reported greater improvements at low stress levels, whereas the TOPS-TO group reported greater improvement at high-stress levels. The TOPS-TO group did not have significantly lower symptoms than the IRC group on any comparison. CONCLUSION: Findings support the efficacy of online family problem solving to address executive dysfunction and improve externalizing behaviors among youth with TBI from less advantaged households. Treatment with the teen alone may be indicated in high-stress families.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".