Creating a Healthy Built Environment for Diabetic Patients: The Case Study of the Eastern Province of the Kingdom of Saudi Arabia
Bibliographic record
Abstract
Many studies worldwide have demonstrated the negative impact of an unhealthy built environment on citizens. In the case of diabetes, studies have concentrated on the environmental impact and accessibility issues of a place i.e. the home and neighborhood, whereas few studies have addressed the comfort of the type and spatial arrangement of a household and linked it with the prevalence of diabetes. Also, little research has tackled the place's impact on diabetic patients and their views concerning their environments. This paper demonstrates the outcomes of survey that was carried out on diabetic individuals who usually visit the King Fahd teaching hospital of the University of Dammam, Al-Khober, the Kingdom of Saudi Arabia (KSA). The patients were surveyed and physically examined. The present researchers found significant links between patients' diabetes symptoms such as reported paresthesia and blurred vision, and medical investigations results such as lipid profile, blood glucose and blood pressure with the environmental conditions of their homes and neighborhoods. The paper shows that the prevalence of the disease is not only caused by an unhealthy lifestyle but also by an unhealthy built environment. Moreover, it illustrates that unhealthy built environment promotes unhealthy life styles. It makes recommendations on how to improve the built environment in the KSA to be healthier for all citizens including the diabetic patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".