A scientometric analysis of music therapy in pediatrics settings globally: Research trends, collaboration networks, and emerging topics (2000–2024)
Bibliographic record
Abstract
Music therapy (MT) has emerged as an effective non-pharmacological intervention for enhancing emotional regulation, social communication, and neurocognitive function in children. This study provides the first comprehensive scientometric analysis of global MT research trends in pediatric settings. Analyzing 1,383 publications from Web of Science, we employed co-citation networks, keyword clustering, and burst detection to: 1) map international collaboration networks, 2) track thematic evolution, and 3) identify emerging frontiers. Results reveal exponential growth since 2010, peaking in 2022, with the U.S., U.K., and Australia as leading contributors. Core research clusters focus on autism spectrum disorder, pediatric anxiety, and pain management, while cutting-edge domains include AI-assisted MT, computational modeling, and neuroplasticity-based interventions. The field shows a paradigm shift from generalized approaches to precision therapies, particularly in pediatric oncology, neonatal care, and neurodevelopmental rehabilitation. Key institutions like Aalborg University and University of Toronto anchor collaborative networks. This analysis not only delineates the intellectual structure of pediatric MT research but also provides empirical guidance for future studies, clinical applications, and cross-disciplinary innovation in this rapidly evolving field. The findings highlight MT's growing scientific validation and its transition toward technology-integrated, evidence-based practices in child healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.046 |
| 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) | 0.000 | 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 teacher head, 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".