The effect of the Quran recitation on mental health of the Iranian medical staff
Bibliographic record
Abstract
This study was conducted with aim of determine the effect of the Quran recitation on mental health of the medical staff of Mazandaran University of Medical Sciences (Sari, Iran). This quasi-experiment study was done in 2016. According to same study 80 medical staff of Mazandaran University of medical sciences (Mazandaran, Iran) were gathered according to inclusion criteria’s. Inclusion criteria’s were (i) complete satisfaction to study, (ii) no having mental or physical disorders. In case of emergency events participants were excluded from study. Then participants randomly distributed to two groups (40 participants in control and experimental group). Experimental group listened to some verses of the Holy Quran for 3 months at the beginning of each working day for 3 minutes, while the control group didn't receive the Quran recitation. The findings showed that 45% of staffs were male and 55% of staffs were women. The average age of staffs was 44.87 ± 3.56. The mean of mental health and all its domains, after hearing the verses of the Quran, in experimental group was higher than the control group (p < .05). Also women indicated a higher average score than men on mental health, behavioral and socio emotional domains. As a final conclusion and on the basis of the findings of this study; it can be said, particularly in Muslim communities, hearing the Quran recitations improves the mental state of the people. Therefore, it is recommended to use the Quran recitations to reinforce positive emotions and psychological comfort for Muslim staffs.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".