Knowledge and Awareness of Rare Diseases Among Healthcare Professionals in the Kingdom of Bahrain
Bibliographic record
Abstract
Aim Recent studies highlighted that lack of knowledge on rare diseases is a problem that requires attention. This study aims to assess healthcare professionals' general awareness and knowledge of rare diseases in a tertiary hospital in the Kingdom of Bahrain. Method The study employed a cross-sectional design, utilizing a survey questionnaire derived from the most recent literature. The survey encompassed socio-demographic factors and quiz-based questions that were previously created by Domaradzi and Walkowiak to assess knowledge and awareness of rare diseases. To ensure convenience and accessibility, the survey was made available in both Arabic and English languages. Results Of a total of 333 responses, 25.2% were physicians, 53.8% were nurses, and 21.0% were allied health personnel. The majority of participants (87.4%) were aware of and had heard the term "rare diseases" prior to this survey. Participants were able to recognize what age group is frequently affected by rare diseases (p=0.023) and what the common cause of rare diseases worldwide is (p<0.001). Overall scores showed that only four participants answered all questions correctly, testing their knowledge of rare diseases. There was a weak correlation between self-declared knowledge and the overall score achieved (r=0.190; p<0.001), which indicates that the population's self-declared knowledge did not portray their actual knowledge of rare diseases. Conclusion This study highlights the need for improved knowledge of rare diseases among healthcare professionals, which aligns with the global knowledge landscape. To bridge the knowledge gap, we recommend action plans to ensure that healthcare professionals have rich knowledge of rare diseases and further improve patient care. Additionally, enhancing advocacy efforts is crucial to ensure optimal local and global patient care services.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".