Primary Care Physicians’ Knowledge, Perceptions, and Comfort Level in Managing Patients Fasting in Ramadan
Bibliographic record
Abstract
Background: Once a year, Muslims fast from dawn to sunset during the month of Ramadan. While fasting has many positive health implications, it may pose risks to individuals with underlying health issues. Despite the exemption from fasting for those who are ill, many Muslims with chronic conditions choose to fast. It is unclear how many Muslim patients receive counseling on fasting. As such, the purpose of this pilot project was to assess the knowledge, perception, and comfort level of primary care physicians (PCPs) at Dalhousie University’s Department of Family Medicine in managing patients choosing to fast during Ramadan. Methods: A 16-item anonymous, self-administered, structured online survey was distributed to PCPs with an academic affiliation with the Department of Family Medicine at Dalhousie University. Participants rated their level of comfort, objective knowledge, and perceptions of managing patients fasting in Ramadan. Results: Many PCPs perceived the importance of understanding Ramadan fasting and its relevance to their patients’ health, however, they did not have adequate knowledge about the matter. The majority of PCPs felt they received inadequate training in this area and did not feel comfortable counseling and managing the health of these patients. Conclusions: The findings of this study have outlined a knowledge gap that exists within our PCP community and will help inform and prioritize educational needs and direct efforts to ensure safe patient management during Ramadan.
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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 | 0.004 |
| 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".