Parent training for disruptive behavior symptoms in attention deficit hyperactivity disorder: a randomized clinical trial
Bibliographic record
Abstract
Background Attention-Deficit/Hyperactivity Disorder (ADHD) affects 5% of children and 2.5% of adults worldwide. Comorbidities are frequent, and Oppositional Defiant Disorder (ODD) reaches 50%. Family environment is crucial for the severity of behaviors and for prognosis. In middle-income countries, access to treatment is challenging, with more untreated children than those under treatment. Face-to-face behavioral parent training (PT) is a well-established intervention to improve child behavior and parenting. Method A clinical trial was designed to compare PT-online and face-to-face effects to a waiting list group. Outcomes were the ADHD and ODD symptoms, parental stress and styles, and quality of life. Families were allocated into three groups: standard treatment (ST), ST + PT online, and ST + Face-to-Face PT. We used repeated measures ANOVA for pre × post treatment analysis corrected for multiple comparisons. Results and discussion Parent training was effective in reducing symptoms of ADHD ( p = 0.030) and ODD ( p = 0.026) irrespective of modality ( p = 1.000). The combination of ST and PT was also associated with better quality of life in the physical domain for patients ( p = 0.009) and their parents ( p = 0.050). In addition to preliminary data, online intervention seems effective for parenting and improving social acceptance of children. The potential to reach many by an online strategy with a self-directed platform may imply effectiveness with a low cost for public health to support parents’ symptoms management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".