Empowering End‐of‐Life Decision‐Making: Utilizing Brochures to Support Muslim Patients and Families in Canada
Bibliographic record
Abstract
Canada is a multicultural country with diversity across faiths and ethnicities. Although Islam is the second-largest reported religion, healthcare providers often lack familiarity with Islamic beliefs, values, and how they impact end-of-life decision-making compared to the more commonly encountered faiths, such as Christianity. For Muslims, guidance on end-of-life decision-making is sought from the teachings of the Quran, Sunna, and Islamic rulings. However, decision-making may be influenced by a lack of awareness among Muslim patients, their families, and healthcare providers regarding the Islamic permissibility and prohibitions related to end-of-life care. Additionally, there may be a limited understanding of the prevailing healthcare legislation that structures end-of-life care in Canada. This adds to the existing barriers faced by Muslim patients and their families when making decisions that are both religiously and culturally informed while residing in a non-Muslim majority country and receiving care from non-Muslim providers. With the goal of increasing awareness, promoting autonomy, and empowering patients to actively participate in their healthcare and make informed decisions, this paper explores the development and potential value of a decision-making tool: a brochure that integrates Islamic perspectives on end-of-life care with the Canadian healthcare legislation and policy. Although the brochure is primarily intended for Muslim patients and families, it also aims to support healthcare providers and raise awareness when a Muslim Chaplain or Imam is unavailable.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".