The effect of therapeutic communication based on Peplau’s model on body image and pain among cancer patients undergoing radiotherapy in the Parsian hospital of Shahrekord
Bibliographic record
Abstract
Background and aims: Cancer has significant effects on the lives of cancer patients and their families, so effective communication skills are an integral part of the process of caring. This study aims to investigate the effect of Peplau’s Therapeutic communication model on body image and pain among cancer patients. Methods: The present research is a quasi-experimental study conducted with the attendance of 64 cancer patients undergoing radiotherapy in Shahrekord Parsian Hospital in 2020-2021. First, the research units were selected purposefully and then randomly assigned to intervention and control groups. In the intervention group, Peplau’s nursing model was done in four stages individually. Data collection tools were a demographic survey questionnaire, McGill Pain Questionnaire, and Body Image Questionnaire (MBSRQ). The mean scores before, immediately after, and three months after were compared using SPPS version 24 software. Results: Before interfering, the results of the study indicated the standard deviation±mean of body image scores in control and intervention groups were respectively 204.81±2.79, 206 (217-75.187)+that were not significantly different from each other (P≥0.568). Whereas these scores immediately and three months after had statistically significant differences from each other (P<0.01). The results also indicated that the Standard deviation±mean of pain scores in control and intervention groups were 59.56±0.793 and 58.25±0.627 were not significantly different (P≥0.248). However, these scores immediately and three months after had statistically significant differences from each other (P<0.01). Conclusion: The findings of this research showed that implementing Peplau’s theory can improve body image and decrease pain in patients. Due to this program’s effectiveness, low cost, and safety, it is recommended for consideration in the nursing care program
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".