Bibliographic record
Abstract
OBJECTIVE: We aim to review existing literature on the effects of background music in waiting rooms on patients. Furthermore, we examine existing neurobiological research for potential mechanisms by which music may affect patients. BACKGROUND: Music has been studied in healthcare in various forms, from formal interventions such as music therapy to passive listening as therapy. However, music is also present in the healthcare environment in the form of background music in waiting rooms. There has been interest in whether background music in such a setting may have beneficial effects on patient anxiety in order to potentially inform healthcare workers whether and what type of music may be suitable for waiting rooms. METHODS: We reviewed existing literature on music in healthcare waiting rooms and the neurobiological mechanisms by which music affects anxiety. RESULTS: We located several small studies performed in a range of settings, including physician office waiting rooms and preoperative waiting areas. The studies generally reported that most patients viewed music in these areas positively; some, but not all, studies showed positive effects on patient anxiety. A variety of theories by which music may impact patient anxiety was noted. CONCLUSIONS: We conclude that there exists some evidence to support an anxiety-reducing effect of background music on patients, though studies vary widely in methodology and music selection. A small amount of neurobiological research into the pertinent mechanisms has been conducted, but further research will be required to elucidate the exact mechanisms by which this intervention may reduce anxiety.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".