The Effects of Massage Therapy on Pain Management in the Acute Care Setting
Bibliographic record
Abstract
BACKGROUND: Pain management remains a critical issue for hospitals and is receiving the attention of hospital accreditation organizations. The acute care setting of the hospital provides an excellent opportunity for the integration of massage therapy for pain management into the team-centered approach of patient care. PURPOSE AND SETTING: This preliminary study evaluated the effect of the use of massage therapy on inpatient pain levels in the acute care setting. The study was conducted at Flagstaff Medical Center in Flagstaff, Arizona-a nonprofit community hospital serving a large rural area of northern Arizona. METHOD: A convenience sample was used to identify research participants. Pain levels before and after massage therapy were recorded using a 0 - 10 visual analog scale. Quantitative and qualitative methods were used for analysis of this descriptive study. PARTICIPANTS: Hospital inpatients (n = 53) from medical, surgical, and obstetrics units participated in the current research by each receiving one or more massage therapy sessions averaging 30 minutes each. The number of sessions received depended on the length of the hospital stay. RESULT: Before massage, the mean pain level recorded by the patients was 5.18 [standard deviation (SD): 2.01]. After massage, the mean pain level was 2.33 (SD: 2.10). The observed reduction in pain was statistically significant: paired samples t(52) = 12.43, r = .67, d = 1.38, p < .001. Qualitative data illustrated improvement in all areas, with the most significant areas of impact reported being overall pain level, emotional well-being, relaxation, and ability to sleep. CONCLUSIONS: This study shows that integration of massage therapy into the acute care setting creates overall positive results in the patient's ability to deal with the challenging physical and psychological aspects of their health condition. The study demonstrated not only significant reduction in pain levels, but also the interrelatedness of pain, relaxation, sleep, emotions, recovery, and finally, the healing process.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".