Effect of music on chronic osteoarthritis pain in older people
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis is the most common degenerative disease in humans. It usually begins in middle age and is progressive. Chronic pain in older people presents a significant obstacle in maintaining function and independence. Previous studies have shown that music can improve motivation, elevate mood, and increase feelings of control in older people. PURPOSE: The purpose of this randomized clinical trial was to examine the influence of music as a nursing intervention on osteoarthritis pain in elders. METHOD: Data were collected using the short form of the McGill Pain Questionnaire with 66 elders suffering from chronic osteoarthritis pain. Differences in perceptions of pain were measured over 14 days in an experimental group who listened to music for 20 minutes daily and a control group who sat quietly for 20 minutes daily. All participants completed the Short Form McGill Pain Questionnaire (SF-MPQ) on day 1, 7, and 14 of the study. RESULTS: Results of t-tests indicated that those who listened to music had less pain on both the Pain Rating Index on day 1 (P = 0.001), day 7 (P = 0.001) and day 14 (P = 0.001) and on the Visual Analogue Scale on day 1 (P = 0.001), day 7 (P = 0.001) and day 14 (P = 0.001), when compared with those who sat quietly and did not listen to music. A repeated measure analysis of variance controlling for pretest measures demonstrated a significant decrease in pain among experimental group participants when compared with the control group on the pain descriptor section of the SF-MPQ (P = 0.001) and the visual analogue portion of the SF-MPQ (P = 0.001). CONCLUSION: Listening to music was an effective nursing intervention for the reduction of chronic osteoarthritis pain in the community-dwelling elders in this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".