Efficacy of cognitive-behavioral therapy and deep relaxation for children with attention-deficit hyperactivity disorder
Bibliographic record
Abstract
Objective:This study made a preliminary exploration of the efficacy of physically-based cognitive-behavioral therapy and deep relaxation for children with attention-deficit hyperactivity disorder (ADHD).Methods:ADHD behavior and cognitive functions were assessed by test D2 and Conner’s scale before and after a one-year physically-based training program. The reliability of test scores was assessed by repeat testing of a control group (CG) of 10 students who did not have ADHD. Children (10 per group) with ADHD were assigned to physically-based cognitive-behavioral therapy and deep relaxation (E1) or physically-based cognitive-behavioral alone (E2).Results:After 52 weeks of treatment, an intra-group comparison showed that E1 and E2 had improved their scores on the test D2, whereas CG showed no significant change. In addition, most participants with ADHD showed a remarkable improvement in their attentional behavior, with group E1 responding better to treatment than group E2.Conclusions:Physically-based cognitive behavioral therapy appears to improve function and social competence in children with ADHD, and should be recommended as an alternative or supplement to pharmaceutical 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".