Global Research Trends in CBT for Children with ASD: A Bibliometric Analysis of Disparities Between Developed and Developing Countries
Bibliographic record
Abstract
ntroduction: Autism spectrum disorder (ASD) is a neurodevelopmental condition with an increasing global prevalence that demands effective intervention strategies. Cognitive behavioral therapy (CBT) is a well-researched approach for treating various co-occurring disorders in children with ASD. However, despite the growing volume of research, there remains a lack of comprehensive bibliometric analysis that maps global trends, thematic focus, and collaboration patterns in CBT-related ASD studies, highlighting a critical gap that this study seeks to address. Methods: This study used a bibliometric approach to analyze global research trends in CBT for treating children with ASD. It analyzed 148 articles published between 2007 and 2025. Data were collected from the Scopus database and analyzed using VOSviewer, Harzing's Publish or Perish, and Microsoft Excel. This bibliometric analysis provides a novel perspective by quantitatively mapping the evolution of research, identifying underexplored themes, and revealing disparities in scientific contributions between developed and developing countries, an area that has received limited attention in existing CBT-ASD literature. Results: The results showed a significant upward trend in publications on CBT for ASD since 2007, peaking by 2025. The United States, the United Kingdom, Canada, and Australia dominated the geographical distribution of research, whereas developing countries had limited contributions. Collaboration patterns revealed strong connections between developed countries, with the United States as the main center. This study focused on the effectiveness of CBT in managing anxiety disorders, improving social skills, and addressing comorbidities, such as obsessive-compulsive disorder and sleep problems. However, there are research gaps in the application of CBT for severe ASD and in the involvement of developing countries. Conclusion: This study offers a novel bibliometric insight into the evolution and focus of CBT research in ASD, highlighting underexplored areas for future inquiry. Its implications can be extended to health practitioners, policymakers, educators, and researchers. Future research should explore the adaptation of CBT to various cultural contexts and ASD severity, utilizing technological advancements and multidisciplinary collaboration to provide more inclusive and accessible interventions for children with ASD worldwide.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.181 | 0.240 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".