The Role of Islamic Beliefs in Facilitating Acceptance of Cancer Diagnosis
Bibliographic record
Abstract
Although survival rates for patients with cancer have increased, this disease continues to affect couples significantly. Religion and culture seem to be part of the therapeutic process for people with cancer. Despite the abundance of the Arab Muslim community in Western countries, there is a lack of documented data on Arab Muslim couples experiencing cancer. A simple exploratory qualitative study was conducted through semi-structured interviews on six married couples (n = 12) identifying with the Arab Muslim culture and being affected by cancer. An iterative data analysis was performed. Results were reported under the following themes: accepting illness through coping strategies provided by Muslim religious beliefs and practices, experiencing problems with the expression of needs and feelings within the couple, experiencing closeness within the family, and experiencing illness in the hospital setting as Muslims. Our results show that Islamic beliefs can facilitate acceptance of a cancer diagnosis. It is also noted that religion seems to unite spouses in supporting each other and maintaining hope in a difficult context. Communication issues may persist between a couple due to stressors related to cancer. The results of this study could raise awareness about the importance of exploring religious and spiritual beliefs when supporting couples affected by cancer.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".