Low-Intensity Cognitive Behavioural Therapy-Based Music Group (CBT-Music) for the Treatment of Symptoms of Anxiety and Depression: A Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Music has the potential to be an effective and engaging therapeutic intervention in the treatment of mental illness. This research area remains underdeveloped. AIMS: This paper reports the feasibility of an innovative low-intensity CBT-based music (CBT-Music) group targeted to symptoms of depression and anxiety. METHOD: A total of 28 participants with symptoms of depression and anxiety who were attending community mental health services were recruited for the study and randomized into TAU (treatment as usual) plus low-intensity CBT-Music (treatment) or to TAU alone (control). The treatment group consisted of a 9-week music group that incorporated various components of CBT material into a musical context. Feasibility was the primary outcome. The secondary outcomes were a reduction in depression, anxiety (Hospital Anxiety and Depression Scale) and disability (WHO Disability Assessment Schedule 2.0) assessed at baseline and 10 weeks. RESULTS: Recruitment proved feasible, retention rates were high, and the participants reported a high level of acceptability. A randomized control study design was successfully implemented as there were no significant differences between treatment and control groups at baseline. Participants in the treatment group showed improvement in disability (p = 0.027). Despite a reduction in depression and anxiety scores, these differences were not statistically significant. CONCLUSIONS: A low-intensity CBT-based music group can be successfully administered to clients of community mental health services. There are indications of effectiveness in reducing disability, although there appears to be negligible effect on symptoms of anxiety and depression. This is the first report of a trial of a low-intensity CBT-based music group intervention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".