The Effectiveness of Cognitive-Existential Group Therapy on Increasing Hope and Decreasing Depression in Women-Treated With Haemodialysis
Bibliographic record
Abstract
INTRODUCTION: Hopefulness is one of the most significant predictors of adaptation in hemodialysis patients, and plays a vital role in the recovery process. In contrast to hopefulness, depression is a frequent psychological reaction of the hemodialysis treatment with many negative consequences. The current research was designed to examine the effect of cognitive-existential treatment on the level of hopefulness and depression in hemodialysis patients. MATERIALS & METHODS: This quasi-experimental research included 22 female patients suffering from chronic kidney failure disease undergoing hemodialysis treatment for at least 3 months. The patients were randomly assigned into two groups of experimental and control conditions. The experimental group received a combination of treatment including some elements of "existentialism" philosophy and a "cognitive" approach designed for the Iranian population. The treatment protocol lasted for 12 sessions of 90 minutes twice per week prior to the entry of the patient to the dialysis session. Miller's hope scale and BDI-II-21 were employed to collect the data. Statistical analysis was performed on the data using analysis of covariance by SPSS: 16 software. RESULTS: The result of the analysis indicated that there was a significant improvement in hopefulness level and decrease in depression of the patients in the experiment condition (P<0.01). CONCLUSION: The result of analysis showed that cognitive-existential treatment resulted in the increase of hopefulness and decrease level of depression in the hemodialysis patients suffering from chronic kidney failure.
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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) | 0.001 | 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".