Characteristics of sleep-conducive music: A narrative evidence review
Bibliographic record
Abstract
Objectives: Sleep deficiency (SD) is a prevalent problem and has serious negative consequences for physical, cognitive, and psychological well-being. The use of music as a non-pharmacological sleep intervention has been proposed in several studies. A 2014 meta-analysis of 10 randomized trials evaluating the impact of music on sleep concluded that it can decrease sleep onset delay (latency) and sleep disturbances, increases sleep duration, and improves daytime dysfunction. It appears that, to-date, evidence-based guidelines for the selection and/or production of sleep-promoting music do not exist. This review addresses that gap and synthesizes available literature towards the goal of developing guidelines grounded in the evidence-based characteristics of sleep conducive music. Design and Results: A narrative review of research papers relevant to the topic identified evidence-based characteristics of sleep-conducive music related to tempo, rhythm, pitch, volume, and duration. Conclusion: This identification and compilation of evidence-based characteristics of sleep-conducive music can underpin future research that targets development and testing of specific music to promote sleep.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".