Widespread clinical implementation of the teen online problem-solving program: Progress, barriers, and lessons learned
Bibliographic record
Abstract
Objective: We describe the clinical implementation in North America of Teen Online Problem Solving (TOPS), a 10+ session, evidence-based telehealth intervention providing training in problem-solving, emotion regulation, and communication skills. Methods: Twelve children's hospitals and three rehabilitation hospitals participated, agreeing to train a minimum of five therapists to deliver the program and to enroll two patients with traumatic brain injuries (TBI) per month. Barriers to reach and adoption were addressed during monthly calls, resulting in expansion of the program to other neurological conditions and extending training to speech therapists. Results: Over 26 months, 381 patients were enrolled (199 TBI, 182 other brain conditions), and 101 completed the program. A total of 307 therapists were trained, and 58 went on to deliver the program. Institutional, provider, and patient barriers and strategies to address them are discussed. Conclusions: The TOPS implementation process highlights the challenges of implementing complex pediatric neurorehabilitation programs while underscoring potential avenues for improving reach and adoption.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".