Death Anxiety and its Relationship with quality of life in Women with Cancer
Bibliographic record
Abstract
Abstract Background & Aims: Quality of life of patients with cancer is not determined only with the disease and its treatment but it depends on other medical, psychological and social conditions. Death anxiety and psychological disorders can affect the quality of life for this group of patients. The aim of this study was to determine death anxiety and its relationship with quality of life in women with cancer admitted to Kosar Hospital of Qazvin city in 2013. Material & Methods: It was a cross-sectional study that was carried out among 276 women with cancer. Data was collected by questionnaire. It included three parts: demographic characteristics, Templer Death Anxiety Scale and the McGill Quality of Life Scale. Data analysis was performed by descriptive and inferential statistics (Kolmogorov - Smirnov, Spearman correlation test, linear regression) using SPSS-PC (v.20). Results: The median of death anxiety score and the mean score of quality of life were 48 (IQR: 8) and 103.07 ± 25.11 respectively. There was a significant relationship between death anxiety and quality of life (rs=-0.35). Also there was significant correlation between death anxiety and psychological quality of life (rs=-0.38), age (rs=-0.13) and frequency of praying (rs=-0.14). Multivariate linear regression showed that death anxiety, social support and education level are predictive factors of the quality of life in women with cancer. Conclusion: Developing a comprehensive care program for patients with cancer regarding the factors affecting their quality of life would be possible. Reducing death anxiety, increased social support and improved level of education can improve the quality of life of women with cancer. Received: 15 March 2013 Accepted: 12 Jun 2013
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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 | 0.003 |
| 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".