Cognitive-Behavioral Therapy Booster Treatment in Pediatric Obsessive-Compulsive Disorder: A Utilization Assessment Pilot Study
Bibliographic record
Abstract
BACKGROUND: Cognitive-behavioral therapy (CBT) for pediatric obsessivecompulsive disorder (OCD) is effective, although many individuals report they need continued support after completing treatment. METHODS: Six monthly drop-in booster sessions were offered to 94 youth with OCD and their parents who previously had completed a 12-week group family-based CBT program (GF-CBT). This report describes program utilization rates and participant satisfaction levels. RESULTS: Twenty-three percent (n = 22) of invited youths with OCD attended ≥1 booster session; 63% of attendees participated in >1 session. The mean number of attended sessions was 2.84 (standard deviation = 1.74). No significant group differences between booster attendees and non-attendees were found in terms of age, sex, ethnicity, parental education, or symptom severity at baseline or end of GF-CBT. Booster session attendees were more likely to have comorbidities than non-attendees (82% vs 58%; P = .045). Most participants were recent treatment completers (59%). Based on participant feedback, booster sessions were valuable, with perceived benefits related to peer interaction and support, skills review, and homework development. CONCLUSIONS: Cognitive-behavioral therapy booster sessions for pediatric OCD seem to be an acceptable approach that a significant percentage of recent treatment completers would utilize. Further research is needed to examine program efficacy and to draw conclusions about key program features.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".