Diabetes and driving recommendations among healthcare providers in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVES: To assess healthcare providers' knowledge and awareness of the recommendations for drivers with insulin-treated diabetes in Saudi Arabia. Methods: A cross-sectional study was conducted among healthcare providers working at 4 tertiary hospitals in Riyadh, Saudi Arabia between April 2016 and December 2016 using a self-administered questionnaire. Results: A total of 285 healthcare providers completed the survey (response rate 88.5%). Most (70.2%) were aware of the safe driving recommendations for patients with insulin-treated diabetes. However, the need to check blood glucose levels before driving was underestimated by almost one-third (30.2%). Only one-quarter (24.6%) identified the correct level of blood glucose level that is safe for a patient when driving, and 28.4% identified the recommended time for checking blood glucose before driving. Participants who were aware of the recommendations for safe driving had a significantly higher average knowledge score (68.8%) than those who were not aware (58.8%; p less than 0.001). There was a significant difference in the average knowledge score among medical specialties (p=0.002) and job levels (p less than 0.001). Conclusions: Most healthcare providers identified the importance of evaluating their patients for ability to drive safely, but we found some important areas of knowledge deficit. Professional intervention to improve healthcare providers' awareness and knowledge regarding diabetes and driving is the first step in improving detection and reporting high-risk drivers with diabetes to prevent future driving mishaps.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".