The Impact of Built Environment on Diabetic Patients: The Case of Eastern Province, Kingdom of Saudi Arabia
Bibliographic record
Abstract
At present, Diabetes mellitus is considered as one of the main threats to the human health in the 21st century. It may lead to severe conditions such as blindness, end-stage of renal disease, limb amputation and a variety of debilitating neuropathies. Previous researches indicated that diabetes is caused by a complex interaction of patient's genetics, life-style and environmental factors. They also highlighted that providing quality and healthy built environment to citizens including diabetic patients would prevent poor and unhealthy condition. The Kingdom of Saudi Arabia (KSA) is one of top ten countries in the prevalence of diabetes. Little researches though were conducted in KSA in regards to the effect of environmental conditions of the built environment. In 2011, the present researchers have carried out a pilot survey on a number of diabetic patients to find out the possible impact of built environment settings on the patient's lifestyle. The research explored whether diabetic patients use smart tools in their daily life to overcome the daily life's difficulties and perform their life as normal as possible. The results showed a close link between a poor home and environmental settings, the patient's lifestyle, and the patient's health status. It also highlighted the absence of smart tools and systems use. The paper argues that certain changes to the built environment must be done and to provide a healthy and safe environment for diabetic patients. This would help these patients to abandon their bad habits and adopt healthier lifestyle.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".