The Use of Music to Manage Burnout in Nurses: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it