Music engagement for stress and anxiety in adults during the COVID-19 pandemic: A systematic review
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused heightened mental distress globally. The purpose of this systematic review is to investigate the impact of music engagement on stress and anxiety in adults during COVID-19. Thirteen articles were included, encompassing 9,893 adults and reporting on seven forms of music engagement: music listening, singing, playing an instrument, watching music videos or virtual performances, dancing to music, composing, and externally-facilitated music interventions. The majority of articles concluded a beneficial impact of music on stress and anxiety among adults during the COVID-19 pandemic. Of the seven studies that investigated stress, four (57.1%) reported that music had a positive impact on stress and, of the nine studies that investigated anxiety, six (66.7%) reported a positive impact on anxiety. A higher proportion of externally-facilitated music studies reported reductions in stress and anxiety compared to studies with participant-facilitated music interventions. Our systematic review demonstrates the potential feasibility of music to improve mental health outcomes during times of heightened psychological distress. However, given the limited quality of included articles and the high proportion of observational studies, further research is required to better elucidate the effect of music on stress and anxiety among adults during the COVID-19 pandemic.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".