Religious Wellbeing as a Predictor for Quality of Life in Iranian Hemodialysis Patients
Bibliographic record
Abstract
AIM: Spiritual well-being is known as a main resource for adjustment and coping when confronted with stressful situations such as managing a chronic disease. The aim of this study is to determine the very relationship between spiritual well-being and quality of life in hemodialysis patients. METHODS: A convenience sample of 95 patients with end-stage renal disease who were referred to main hemodialysis centers were included from December 2012 to June 2013. Data was collected by using a socio-demographic questionnaire, the SF-36 quality of life scale, and the spiritual wellbeing scale. Descriptive analysis, Pearson's correlation and logistic regression analysis were performed for statistical assessment. RESULTS: The mean age of the patients evaluated was 50.4 (SD=15.72) years of age, and 61.1% of the patients were male. Both religious and existential domains of spiritual wellbeing were associated with bodily pain, vitality, social functioning and mental health (P < 0.05). The results of multiple logistic regression showed that religious well-being was associated with better quality of life in both domains of physical (OR=1.17; p=0.01) and mental (OR=1.14; p=0.02) components after controlling for socio-demographic and clinical variables. CONCLUSION: Religious well-being should be considered important predictive factors for the better quality of life in hemodialysis patients. This indicates the need for psychosocial and spiritual supports in the care of these patients.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".