Healthcare professionals knowledge, attitude, practices, and perspectives providing care to Muslims in Western countries who fast during Ramadan: a scoping review
Bibliographic record
Abstract
The practice of fasting during the month of Ramadan is an obligation for healthy Muslims and involves abstaining from food and drinks from dawn to dusk for 29-30 consecutive days annually. With changes in dietary and lifestyle patterns, healthcare professionals (HCPs) play a significant role in supporting Muslims health during Ramadan. In this scoping review, we employed a systematic approach to map existing literature on HCPs' knowledge, attitude, practices, and perspectives working with Muslims who fast during Ramadan in Western countries. Our aim was to identify research gaps and opportunities for improving healthcare services for Muslims during Ramadan. Literature searches were generated through multiple scientific literature databases, including Web of Science, Ovid MEDLINE, CINAHL, and Embase and reviewed following The Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews guidelines. From the eight sources included in this review, it was found that HCPs' knowledge of Ramadan fasting practices vary, with many lacking adequate knowledge. While HCPs recognize potential health complications, adjustments to medications for fasting patients, especially those with diabetes, are often neglected. Challenges in care included language barriers, limited cultural training, and resource awareness. Strategies identified to address barriers include reducing language barriers, providing resources in relevant languages, and enhancing cultural competence training. Further research is required on HCPs' knowledge providing care to Muslims during Ramadan, cultural competency training impact, and diverse healthcare interventions for fasting Muslims. Addressing these gaps may enhance culturally safe care and improve patient outcomes.
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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 | 0.014 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".