The Use of Music to Manage Burnout in Nurses: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: There is a high prevalence of burnout in nurses. This systematic review investigates the use of music to manage burnout in nurses. DATA SOURCE: MEDLINE (Ovid), MEDLINE InProcess/ePubs, Embase, APA PsycINFO, the Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov databases were searched. STUDY INCLUSION AND EXCLUSION CRITERIA: Full-text articles were selected if the study assessed the use of music to manage burnout in nurses. Burnout was defined according to the International Classification of Diseases 11th Revision. DATA EXTRACTION: Data were extracted using an Excel sheet. The second and third authors independently extracted study characteristics, frequency and type of music engagement, measures of burnout, and burnout outcomes (occupational stress, coping with stress, and related symptoms such as anxiety). DATA SYNTHESIS: Study and outcome data were summarized. RESULTS: The literature search resulted in 2210 articles and 16 articles were included (n = 1205 nurses). All seven cross-sectional studies reported upon nurses' self-facilitated use of music including music listening, playing instruments, and music entertainment for coping or preventing stress, supporting wellbeing, or enhancing work engagement. Externally-facilitated music engagement, including music listening, chanting, percussive improvisation, and song writing, was reported in the four randomized controlled trials and five cohort studies with reductions in burnout outcomes. CONCLUSIONS: Self-facilitated and externally-facilitated music engagement can help to reduce burnout in nurses.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".