Music as nursing intervention for pain in five Asian countries*
Bibliographic record
Abstract
BACKGROUND: The use of music as intervention for relieving pain has increased in recent years, prompting its growing use among the people of the western world. However, among Asians, music has long been used for this purpose and continues to be so today. Despite this common knowledge, Asians have not generally written about the therapeutic effects of music. Consequently, most of the published research studies supporting this claim were conducted in western settings using western music. PURPOSE: To describe the use of music as intervention in painful conditions as experienced by people in five Asian countries: China, Thailand, Philippines, South Korea and Taiwan. METHOD: Descriptive survey of studies using music as intervention for painful conditions conducted in selected five Asian countries. FINDINGS: Twelve studies including theses and dissertations, published and unpublished, were found; however, only nine met the inclusion criteria. Data were categorized according to research design, sample size, gender, age, duration of music, frequency of music intervention, types of pain and instruments used to measure pain, conceptual or theoretical frameworks and statistical significance of the study. Five of the nine studies declared significant decrease in pain, while three reported mixed results. Fundamentally, the findings of the studies suggested that with music, relief of pain was possible. IMPLICATIONS: The mixed results imply the need for further investigation of the effects of music in painful conditions. Thus, the authors suggest continuing studies on the effects of music as intervention in painful conditions, and encourage increasing global dissemination of these studies, particularly in international journals.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".