Islamic Ethical Considerations on Medical Decision-Making in Adolescence
Bibliographic record
Abstract
Medical decision-making in adolescence has not been studied in-depth from the Islamic bioethics perspective. The objective of this dissertation is to use the Islamic ethical position to explore the adolescent medical decision-making process in Canada so as to contribute to building frameworks for Islamic bioethics consumers such as patients, physicians and policymakers. A descriptive literature review is conducted to analyze data from related disciplines such as Islamic theology, developmental psychology, law and clinical ethics through principles of Islamic ethics such as objectives of Sharīʿa, legal maxims and operational maxims. The concepts of taklīf, ahliyya, bulūgh and rushd are focused on due to their criticality in judging moral, religious and legal obligations of the adolescent, as well as adolescents’ decision-making capacity. Our research shows that approaching the process of adolescents’ medical decision-making in Canada from an Islamic ethics perspective involves certain factors. These include intention of medical intervention (ḍarūrī, taḥsīnī or hājī), adolescents’ competence and emotional maturity, potential benefit/harm of the procedure, and their family’s role in decision-making. A guideline using Islamic ethics featuring ‘questions to ask’ is then provided for healthcare workers regarding adolescents’ medical decision-making in Canada.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| 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".