Effect of music on power, pain, depression and disability
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
AIM: This paper reports a study testing the effect of music on power, pain, depression and disability, and comparing the effects of researcher-provided music (standard music) with subject-preferred music (patterning music). BACKGROUND: Chronic non-malignant pain is characterized by pain that persists in spite of traditional interventions. Previous studies have found music to be effective in decreasing pain and anxiety related to postoperative, procedural and cancer pain. However, the effect of music on power, pain, depression, and disability in working age adults with chronic non-malignant pain has not been investigated. METHOD: A randomized controlled clinical trial was carried out with a convenience sample of 60 African American and Caucasian people aged 21-65 years with chronic non-malignant pain. They were randomly assigned to a standard music group (n = 22), patterning music group (n = 18) or control group (n = 20). Pain was measured with the McGill Pain Questionnaire short form; depression was measured with the Center for Epidemiology Studies Depression scale; disability was measured with the Pain Disability Index; and power was measured with the Power as Knowing Participation in Change Tool (version II). RESULTS: The music groups had more power and less pain, depression and disability than the control group, but there were no statistically significant differences between the two music interventions. The model predicting both a direct and indirect effect for music was supported. CONCLUSION: Nurses can teach patients how to use music to enhance the effects of analgesics, decrease pain, depression and disability, and promote feelings of power.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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