Knowledge of Sleep Disorders Among Physicians at a Tertiary Care Hospital in Qatar: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Sleep disorders constitute a major health problem because of their relatively high and rising prevalence. Several studies worldwide have analyzed health care providers' knowledge of sleep disorders. OBJECTIVE: In this study, we aimed to assess the knowledge of sleep disorders among physicians in Qatar. METHODS: A total of 250 physicians were surveyed regarding their knowledge of sleep medicine by using the validated 30-item Assessment of Sleep Knowledge in Medical Education (ASKME) Survey. The participants included residents, fellows, and consultants in medicine and allied subspecialties. A high score was defined as ≥60% of correctly answered questions, implying the respondent has adequate knowledge of sleep disorders. RESULTS: Responses were received from 158 of the 250 physicians, with a response rate of 63.2%. This included responses from 34 residents, 74 clinical fellows, and 50 consultants. The overall mean score was 15.53 (SD 4.42), with the highest possible score of 30. Only 57 of 158 (36.1%) respondents were able to answer ≥60% of the questions correctly. No statistically significant difference was found in the scores of participants with regard to their ranks (ie, residents, fellows, or consultants) or years of medical training. CONCLUSIONS: This study demonstrates that health care providers in Qatar have decreased awareness and knowledge about sleep medicine, which may reflect reduced emphasis on sleep disorders during medical school and training. Increasing awareness regarding sleep medicine among nonspecialist physicians will allow early detection and treatment of sleep disorders, thereby reducing the morbidity associated with these disorders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".