The Relationships between Self-Care and Pain Perception: Experience in Iranian Patients with Cancer
Bibliographic record
Abstract
Background: The present study aimed to investigate the relationship between self-care and pain control in patients with cancer. Methods: In this cross-sectional study (October to December, 2015) 380 cancer patients were admitted to one of the hospitals affiliated to Mazandaran University of Medical Sciences (Sari, Iran) entered to the study using simple random sampling. Data was collected by a demographic questionnaire, self-care scale and McGill pain questionnaire. The statistical package for social sciences, version 20.0 (SPSS Inc., Chicago, IL, USA) was utilized for data analysis by descriptive and infernal statistic tests (Spearman’s correlation and Generalized Linear models). Results: Males in the study (48.39 ± 13 ± 39; CI95: 46.41 - 50.38) were older than females (45.33 ± 18.44; CI95: 42.79 - 47.87). Based on the results of the processing of the Generalized Linear Models, there was not a significant relationship between pain perception and self-care in cancer patients (P > 0.05). But there was a significant relationship between pain perception and its two subsets of physical self-care (B = -1.102, P < 0.001) and emotional self -care (B = 0.823, P < 0.001). Conclusions: Considering the adverse effects of chronic pain treatment process and secondary problems, more comprehensive studies must be done about the effects of self-care behaviors on the perception of pain so that effective steps can be taken to intervene and promote the health of these patients.
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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 | 0.002 |
| 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) | 0.001 | 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".