THE EFFECTIVENESS OF PAIN MANAGEMENT PROGRAM ON INTENSITY OF PAIN AND QUALITY OF LIFE AMONG CANCER PATIENTS IN MYANMAR
Bibliographic record
Abstract
Introduction: Cancer is one of the leading causes of death worldwide and rapidly becoming a global pandemic. Cancer pain significantly affects the diagnosis, quality of life and survival of patients with cancer. The aim of this study was to analyze the effect of Pain Management Program (PMP) on pain and quality of life in patient with cancer. Methods: This study used quasy experimental design with randomize pre-post test design approach. Data were collected from cancer patients in No (2) Military Hospital (500-Bedded), Yangon, Myanmar. Patients were recruited by using consecutive sampling method, consisted of 30 respondents (experimental group) and 30 respondents (control group) taken according to the inclusion criteria. Short Form-McGill Pain Questionnaire 2 (SF-MPQ 2) was used to assess the pain, and The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-Core 30 (EORTC QLQ-C30) for quality of life. Result and Analysis: Manova test was used to analyze the effect of PMP, it showed p = 0,002 (pain) and p = 0,000 (quality of life). It means that 1) PMP decreased the pain and 2) PMP increased the quality of life especially in general health scales on patient with cancer. However, there were no significant difference between functional scales and symptomatic scales of quality of life. Discussion and Conclusion: Improvements in quality of life and pain-related cancer suggest that the vicious cycle of chronic pain may be alleviated by PMP (education, distraction and relaxation technique). As we see the results, so that PMP can be the effective treatment to be used by nurse for decreasing pain and increasing quality of life in patients with cancer.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".