Effect of progressive muscle relaxation technique on stress, anxiety, and depression after hysterectomy
Bibliographic record
Abstract
Progressive muscle relaxation (PMR) is one the systematic techniques that could be utilized to obtain a deep state of relaxation. It is an important component of nursing care for gynecological postoperative patients. The aim of this study was to determine the effect of progressive muscle relaxation technique on stress, anxiety and depression after hysterectomy. A quasi experimental research design with a pretest-posttest control group was utilized. The study was conducted at the gynecological ward of National Medical Institution in Damanhour, Albehera Governorate. Collection of data consumed six months from starting of December 2014 until the end of May 2015. It comprised a purposive sample of 80 women who were undergoing abdominal hysterectomy. They were divided into two equal groups (study group and control group). Two tools were utilized to gather the necessary data; a socio-demographic structured interview schedule, and the Depression, anxiety and stress scale (DASS-21). Study results revealed that stress, anxiety and depression were statistically significantly decreased among the study group after the intervention (p = .000). The study concluded that the women who received progressive muscle relaxation technique after hysterectomy demonstrated lower stress, anxiety and depression levels than those who received only the routine nursing care. It is recommended that maternity and gynecological nursing should encourage the utilization of the progressive muscle relaxation technique to patients undergoing hysterectomy to minimize their stress, anxiety and depression.
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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".