Ramadan and health: a scientometric analysis of health literature on Ramadan and fasting
Bibliographic record
Abstract
Aim: During Ramadan month, every year, approximately two billion of Muslims practice fasting and avoid eating, drinking and intercourse from dawn to dusk throughout the world. Although the effects of Ramadan fasting on human health were highly studied in academic literature, there are only limited number of scientometric articles referring to Ramadan and health.Material and Method: We performed a scientometric analysis of “Ramadan and health” publications indexed in Web of Science databases between 1980 and 2019 and found a total of 497 articles.Results: The most published documents were original articles (88.13%). The most studies areas were found to be Religion, Nutrition and Endocrinology, (n=269, 214 and 184 items, respectively). The USA was leading country with 76 items followed by Saudi Arabia, the UK, Turkey and Iran (n=76, 58, 55, 39 and 36 papers, respectively). King Saud University (Saudi Arabia) ranked first in institutions with 21 items. H-index of Ramadan and health literature was measured as 40 and total number of citations was 5837. The most indexed keywords were “Ramadan”, “fasting”, “diabetes”, and “pregnancy”. The USA, the UK, Saudi Arabia and Canada were found as the most collaborative countries.Conclusion: The importance of scientometric studies has been increasing in recent years. We think that this scientometric study data about Ramadan and fasting which are the conditions of the religion of Islam will contribute to scientists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".