The Effect of Stress Management Training on Hope in Hemodialysis Patients
Bibliographic record
Abstract
INTRODUCTION: Chronic renal failure exposes patients to the risk of several complications, which will affect every aspect of patient's life, and eventually his hope. This study aims to determine the effect of stress management group training on hope in hemodialysis patients. METHOD: In this quasi-experimental single-blind study, 50 patients with renal failure undergoing hemodialysis at Motahari Hospital in Jahrom were randomly divided into stress management training and control groups. Sampling was purposive, and patients in stress management training group received 60-minute in-person training by the researcher (in groups of 5 to 8 patients) before dialysis, over 5 sessions, lasting 8 weeks, and a researcher-made training booklet was made available to them in the first session. Patients in the control group received routine training given to all patients in hemodialysis department. Patients' hope was recorded before and after intervention. Data collection tools included demographic details form, checklist of problems of hemodialysis patients and Miller hope scale (MHS). Data were analyzed in SPSS-18, using Chi-square, one-way analysis of variance, and paired t-test. RESULTS: Fifty patients were studied in two groups of 25 each. No significant difference was observed between the two groups in terms of age, gender, or hope before intervention. After 8 weeks of training, hope reduced from 95.92±12.63 to 91.16±11.06 (P=0.404) in the control group, and increased from 97.24±11.16 to 170.96±7.99 (P=0.001) in the stress management training group. Significant differences were observed between the two groups in hope scores after the intervention. CONCLUSION: Stress management training by nurses significantly increased hope in hemodialysis patients. This low cost intervention can be used to improve hope in hemodialysis 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 | 0.001 |
| 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) | 0.002 | 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".