The Effectiveness of Acceptance and Commitment Therapy in Reducing the Symptoms of Attention Deficit Hyperactivity Disorder among Intellectual Gifted Students and Improving their Cognitive Flexibility
Bibliographic record
Abstract
Background: The issue of the presence of attention deficit hyperactivity disorder (ADHD) in intellectually gifted students is a complex one due to the overlap between ADHD symptoms and the characteristics of gifted students. Objective: The current study aimed at reducing ADHD symptoms in intellectually gifted students and improving their cognitive flexibility through Acceptance and Commitment Therapy (ACT) principles and techniques. Methods: The study relied on a quasi-experimental approach to test the effectiveness of ACT principles and techniques in reducing the severity of ADHD symptoms and improving cognitive flexibility. A one-group quasi-experimental design (experimental group) was used with pre- and post-measurement and then follow-up measurement to verify the continuity of the program's effectiveness. The independent variable was the principles and techniques of ACT, while the dependent variables were symptoms of ADHD and cognitive flexibility. The final study sample consisted of (11) intellectually gifted secondary school students with ADHD. Results: The study's results demonstrated the effectiveness of ACT in reducing the severity of ADHD symptoms in intellectually gifted people and improving their cognitive flexibility. Conclusions: This study is one of the first to use ACT to reduce the severity of ADHD symptoms and develop cognitive flexibility among this category of students. The study presented a scale to measure cognitive flexibility among secondary school students and a program based on the principles and techniques of ACT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".