Charting the Future of Inclusive Autism Support: A Global Bibliometric Study on Educational and Transitional Issues
Bibliographic record
Abstract
This study examines global research trends on transitional issues in autism spectrum disorder (ASD) between 2019 and 2024, with emphasis on educational contexts and inclusive practices. Using 243 Scopus-indexed articles, the analysis applied bibliometric techniques to identify key themes, influential works, and collaboration networks. Methods included mapping co-authorship, analyzing frequently used keywords, and assessing citation patterns to explore the intellectual structure and thematic evolution of the field. Findings show that research output is concentrated in Western countries—particularly the United States, United Kingdom, Canada, and Australia—while contributions from regions such as China and other parts of Asia are increasing. Dominant research themes focus on postsecondary education, employment, independent living, and transition planning, underscoring the central role of education in preparing autistic individuals for adulthood. Emerging topics, including inclusive education, neurodiversity, student voice, and self-determination, indicate a shift toward strengths-based, person-centered, and interdisciplinary approaches. These trends highlight a growing commitment to educational systems that address diverse learning needs, promote autonomy, and enhance well-being. However, there remains a need for broader cross-cultural collaboration, inclusion of underrepresented regions, and more targeted strategies to support transitions across different life stages. This study offers a comprehensive overview of the evolving discourse, providing evidence to guide educators, researchers, and policymakers in strengthening inclusive and culturally responsive transition frameworks. By illuminating current patterns and future directions, the findings can inform policies and practices that promote equitable opportunities and lifelong participation for autistic individuals.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.129 | 0.227 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| 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".