Glycemic Control in Diabetic Patients in Saudi Arabia: The Role of Knowledge and Self-Management - A Cross-Sectional Study
Bibliographic record
Abstract
INTRODUCTION: Diabetes mellitus (DM) is serious healthcare concern in Saudi Arabia, with the disease’s prevalence in the country being one of the highest worldwide. This study examines various factors which affect outcomes of patients with DM; namely, medication adherence, diabetes knowledge, self-management behaviours, and glycemic control.METHODS: This is a cross-sectional survey-based study. Participants were patients with a DM diagnosis at King Saud Medical City in Riyadh, Saudi Arabia.RESULTS: Positive associations were found between medication adherence and diabetes knowledge; self-management behaviours (glucose management and healthcare use) and diabetes knowledge; self-management behaviours (dietary control) and fasting blood glucose levels; and age and blood glucose levels (both fasting and HgA1c). No associations were found between diabetes knowledge and glycemic control; or between self-management behaviours and HgA1c levels.CONCLUSION: Having good knowledge of diabetes is associated with medication adherence and healthcare self-management. Healthcare practitioners should consider educating DM patients an integral part of the treatment process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".