Bibliographic record
Abstract
Background and objective: Mental health is one of the aspects of health representing ones self-cognition, being able to adjust with others and environment, balanced growth of personality, and humans emotional insight to make him be able to live coordinately with other human beings and society. With regard to this question that Quran is healing and mercy and has the most perfect schedule for human health, one can attain Gods desirable perfection and eminency by thinking about verses and adapting the path of life with it. So we decided to find out the mental health strategies by this holy book. Methods: This study was performed to determine Quran verses on mental health and we stated these verses and presented strategies to cover the protection of mental health by using library methods and available resources. Results: The obtained results by this study show that mental health security, behavioral, emotional, and social strategies have been mentioned in holy Quran for stating the path of mental health. Conclusion: Using the verses of Quran and stating them helps to improve the protection of mental health. Rules and terms of Quran are coordinated with order of nature and physical and spiritual health. Perfection of any creature including man is in identifying and appearing all his typical talents and also the perfect human being is the one that all his potential talents have become to actuality and all his abilities have become to emergence. Quran provides the means of salvation and perfection of human being by explaining the ways of achieving it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".