Bibliographic record
Abstract
Effect of music on power, pain, depression and disability 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
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.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.824 | 0.479 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".