Patients’ Perceptions and Knowledge of Diabetes and Medications: Implications for Medication Adherence and Glycemic Control in Type 2 Diabetes Patients, Northern Ethiopia
Bibliographic record
Abstract
Globally, adherence to Type 2 diabetes mellitus (T2DM) medications remains suboptimal. There are limited insights, however, on this issue in the northern region of Ethiopia. This cross-sectional study at Alamata General Hospital investigated the interplay between patients' medication beliefs, diabetes knowledge, adherence, and glycemic control. Data collection was done using structured questionnaires and chart reviews, while descriptive and inferential statistics were for the analysis. Among 305 T2DM patients, poor medication adherence was prevalent (44.6%), alongside suboptimal glycemic control (75.7%). Patients diagnosed for over a decade had an adjusted odds ratio (AOR) of 3.87 for nonadherence, while high concern about medication side effects was associated with a 20.63-fold higher nonadherence risk (AOR = 20.63). Low disease awareness increased nonadherence risk by 4.54 times (AOR = 4.54), while a strong belief in medication necessity was protective (AOR = 0.21). Poor glycemic control was associated with educational background, diabetes awareness, monthly income, and treatment modality. Urgently needed are tailored diabetes education programs in Northern Ethiopia to counteract high rates of poor medication adherence (AOR = 3.87) and glycemic control among T2DM patients. Targeted interventions, emphasizing knowledge enhancement and reinforcing positive beliefs, are essential for improving outcomes in this population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".