Review: The effectiveness of musical therapy in improving depression and anxiety symptoms among children and adolescents – a systematic review
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety disorders are considered to be among the greatest burdens of disease in children and adolescents. Recent literature has reported music therapy to be a safe and potentially effective intervention for the treatment of depression and anxiety. This systematic review examined the effectiveness of music therapy in reducing the symptom severity of depression and/or anxiety among children and adolescents. METHODS: Randomized controlled trials (RCTs) were obtained from a systematic search of nine major English databases from inception to January 2021. Studies were categorized by outcome, music therapy technique and follow-up period. RESULTS: Seven RCTs (n = 589) were included. Four RCTs had some concern of bias, while three had high risk of bias. Statistical pooling was not appropriate due to clinical heterogeneity. Four studies (n = 428) favoured music therapy for improvement of depressive symptoms at short- and intermediate-term follow-ups compared to control. One study (n = 106) favoured music therapy for improvement of anxiety symptoms at short-term follow-up compared to control. No studies favoured any control over music therapy for symptom improvement of depression and/or anxiety. CONCLUSION: Limited evidence suggests music therapy is an effective treatment for improving depression and/or anxiety symptom severity in children and adolescents. More high-quality RCTs are needed to address methodological flaws of current studies.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".