The relationship between death anxiety and quality of life in hemodialysis patients
Bibliographic record
Abstract
Background & Aim: Concerns about death may negatively affect health-related quality of life. However, little is known about the relationship between death anxiety and quality of life in life-threatening illnesses especially in hemodialysis patients. This research aimed to determine the relationship between death anxiety and quality of life in hemodialysis patients. Methods & Materials: In this descriptive correlational study, 200 hemodialysis patients were selected via stratified random sampling from hospitals affiliated with Zanjan University of Medical Sciences from April to May 2016. Data collection instruments included a demographic questionnaire, the Templer Death Anxiety Scale and the McGill Quality of Life questionnaire. Data analysis was performed by descriptive statistics, correlation test and linear regression model using SPSS v.22. Results: The average score of death anxiety and quality of life were respectively 46.54±10.85 and 82.55±19.01. There was not a significant relationship between death anxiety and quality of life (P>0.05, r=0.044). In the regression analysis, gender was the only significant predictor for death anxiety. This model explained 11.3% of the variance of death anxiety. Moreover, the results of regression model indicated that social support and religious beliefs were only significant predictors for quality of life in hemodialysis patients, and 17.2% of its variance was explained by this model. Conclusion: In the current study, no significant relationship was observed between death anxiety and quality of life in hemodialysis patients. Therefore, it is suggested that further research should be conducted in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".