Takaful Protection for Mental Health Illness From the Perspective of Maqasid Shariah
Bibliographic record
Abstract
Mental health illness becomes one of the major illnesses in Malaysia aside from heart disease. It was recently reported that 29.2% of Malaysians are suffering from mental health illness which increases threefold from the previous year. Majority of the Malaysians suffering from mental health illness comes from the lowest income group. This shows that the lowest income group has less opportunity to seek treatment due to the cost. Even though other countries have started to offer mental health insurance such as the United States of America, United Kingdom, Australia and recently Singapore, Malaysia is still way behind in offering coverage for mental health illness. Therefore, the objective of this paper is to study the mental health takaful from the perspective of Maqasid shariah. The results from this study show that mental health takaful is able to meet the requirement of Maqasid shariah and preserve the benefits of, and prevent harm to human wellbeing. Furthermore, this study will provide an insight to the takaful industry for developing new products that could help mental health disorder patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".