The Relationship Between Hope and Religious Coping Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND & PURPOSE: Globally, diabetes is one of the most common non-contagious diseases resulting in severe complications. Fostered hope facilitates coping and improves self-care and one of the Factors affecting hope is religious beliefs. This research investigated the level of hope and its relationship with religious coping among Type 2 diabetes patients. MATERIAL & METHODS: This correlation, cross-sectional study was conducted on 150 patients with Type 2 diabetes, who had been referred to the Karaj Diabetes Association during the period, March-June 2011, and selected through purposive sampling. A three-part questionnaire including demographic data, the Herth Hope Index, and a short form of religious coping, was used for data collection. The data were analyzed using descriptive and analytic statistics, including Pearson's correlation coefficient, the t-test, a one-way ANOVA, and a multiple regression analysis. The set significance level was p<0.05. RESULTS: The mean hope score was 34.89 (SD±8.75); most of the subjects (46.7%) showed high levels of hope. Positive religious coping, marital status, and social support significantly affected hope fostering(r=0.897, p =0.000). A significant negative relationship was found between hope and age (r=-0.373, p=0.000), and between hope and negative religious coping (r=-0.749, p=0.000). CONCLUSION: Positive religious coping, married life, and social support significantly affected the development of hope. Moreover, there was a significant positive relationship between positive religious coping and social support. So, strengthening social support could lead to increased levels of positive religious coping and fostering of hope.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".