The effectiveness of Chair Massage on Stress and Pain in Oncology
Bibliographic record
Abstract
Background: There is a high prevalence of moderate-to-high levels of chronic stress among nurses, as well as an occurrence of musculoskeletal disorders. Purpose: To evaluate the effectiveness of chair massage to reduce chronic stress and musculoskeletal pain in the Oncology Nursing team. Setting: Two teaching cancer hospitals, one public and the other private, in São Paulo city, Brazil. Participants: A total of 60 women from the Oncology Nursing team. Research Design: A randomized controlled trial divided into two groups: chair massage and control without intervention. Intervention: The massage group received two chair massage sessions lasting 15 minutes, twice a week, for three weeks. Main Outcome Measure: Reduction of stress and pain measured by the List of Signs and Symptoms (LSS) and the Brief Pain Inventory (BPI), respectively. Results: The average age was 32 (± 5.3) years. There was a reduction of stress measured by the LSS with a statistical difference in the group-time interaction (p < .001), with a Cohen’s d value of 1.21 between groups. The BPI analysis showed a statistically significant difference in the group-time interaction for general activity (p < .008), mood (p < .03), work (p < .000), and sleep (p = .03), with reduced pain interference in these components. Conclusion: Chair massage reduced stress and pain interference in the team’s daily life activities, bringing a positive impact in the context of work stress and pain in Oncology nursing professionals.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".