Comparing the effect of self-care education and pain self-management on the nature of pain and quality of life in patients with sickle cell disease
Bibliographic record
Abstract
Background & Aim: Self-care and self-management, as two effective strategies play an effective role in controlling pain and quality of life. Therefore, considering the characteristics and dimensions of these two concepts, this study aimed to compare the effect of self-care education and pain self-management on the nature of pain and quality of life in patients with sickle cell disease. Methods & Materials: In this clinical trial study, 75 patients with sickle cell disease, referred to the thalassemia clinic of Baqaei hospital 2 in Ahvaz and Shahid Beheshti hospital in Abadan, were selected and randomly divided into three groups of 25 people (self-care, pain self-management and control groups). The two intervention groups (self-care and pain self-management) were divided into groups of five people, and received specific training during four sessions for three months. The nature of pain and quality of life of patients were assessed four times (before the intervention, one month, two months and three months after the intervention) using the McGill Pain Questionnaire and Quality of Life Questionnaire. The SPSS software version 22 was used to analyze the data. Results: The results showed that pain self-management and self-care programs were effective in improving the quality of life and pain of patients with sickle cell. However, there was no statistically significant difference between the two intervention groups in the nature of pain and quality of life. Conclusion: Self-care and pain self-management have similar effects on reducing patients’ pain and improving their quality of life. Clinical trial registry: IRCT20160726029086N5
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".