Benson Relaxation Technique: Reducing Pain Intensity, Anxiety level and Improving Sleep Quality among Patients Undergoing Thoracic Surgery
Bibliographic record
Abstract
Thoracic surgery threatens the integrity of body, such as physical, psychological, social and spiritual aspects and may cause discomfort such as pain response and sleep disturbance. Aim: The study aimed to determine the effect of Benson's relaxation technique on reducing pain intensity, anxiety level and improving sleep quality among patients undergoing thoracic surgery. Design: A quasi experimental research design was utilized. Setting: The study was conducted in thoracic surgery department at Mansoura chest hospital and Mansoura University Hospital- chest department. Subjects: A purposive sample of 160 post thoracic surgery were recruited in this study. Tools: Four tools were used; Tool I: Assessment interview questionnaire sheet includes personal and health relevant data, Tool II: Short Form McGill Pain Questionnaire (SFMPQ), Tool III: Groningen Sleep < /div> Quality Scale (GSQS) and Tool V: Hospital Anxiety and Depression Scale (HADS). Results: There was a statistically significant effect of Benson's Relaxation Technique on reducing pain intensity, anxiety level and improving sleep quality among patients undergoing thoracic surgery. Conclusion: The study concluded that, Benson relaxation technique has a positive improvement in level of pain, quality of sleep and anxiety & depression among the study group who applied Benson's relaxation technique. Also, the most common factors that can affect the sleep quality level for those patients is the pain. Recommendation: This study recommended that, the nurses should pay more attention to Benson's relaxation technique as a simple, cheap and effective technique while taking care of post operative patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".