Diabetes knowledge and glycemic control among patients with type 2 diabetes in Bangladesh
Bibliographic record
Abstract
AIMS: To explore the association between knowledge on diabetes and glycemic control among patients with type 2 diabetes in Bangladesh. METHODS: A cross-sectional study was conducted among 515 patients with type 2 diabetes attending a tertiary hospital in Dhaka, Bangladesh. Trained interviewers were used to collect data on socioeconomic status, time since the onset of diabetes, co-morbidities, anthropometric measurements, blood tests, knowledge and perceptions about the causes, management, and complications of diabetes through face to face interviewers based on a structured questionnaire. Diabetes knowledge was reported using a composite score. Chi square tests and correlation analysis were performed to measure the association between knowledge on diabetes and glycemic control. RESULTS: Overall, 45.6% participants had good, 37.7% moderate and 16.7% poor knowledge on diabetes. The mean composite score was 0.75 ± 0.28 and the proportion of participants with a score of ≤50% was 16.7%. Only 24.3% participants identified physical inactivity as a risk factor for diabetes. Knowledge on diabetes was significantly associated with education, gender, monthly income, duration of diabetes, body mass index, family history of diabetes, and marital status but not with glycated hemoglobin (HbA1c). Correlation matrix showed weak negative association between diabetes knowledge score and glycemic control (p < 0.001). CONCLUSION: Patients with type 2 diabetes in Bangladesh have limited knowledge on the causes, management and risk factors for diabetes, despite receiving professional health education and care in a tertiary diabetic hospital. Strategies to improve the quality of diabetes education and identifying other potential factors for glycemic control are important for ensuring optimum management of diabetes in Bangladesh.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".