Quality of life and its determinants among individuals with type 2 diabetes mellitus in rural Bangladesh
Bibliographic record
Abstract
Abstract Background Type 2 diabetes mellitus (T2DM) is a growing public health issue in Bangladesh, with the rural population facing significant barriers to diagnosis and care. While previous studies conducted in urban areas have examined the health-related quality of life (HRQoL) of people with diabetes, evidence from rural areas remains limited. Methods We analysed data from 1,574 adults with diabetes in rural Bangladesh, from the DMagic cluster randomised trial. HRQoL was assessed using the EQ-5D-3L instrument and the visual analogue scale. HRQoL was compared between people with a previous diagnosis of diabetes and those who were unaware of their condition prior to the study. Multivariable linear regressions and modified Poisson regressions were used to examine the associations between HRQoL and socio-demographic factors, risk behaviours, and comorbidities. Results Results indicate that socio-demographic, economic and health factors are associated with HRQoL. The main factors associated with higher HRQoL and clinically relevant were Higher levels of education, being male and belonging to a higher wealth tertile. Individuals with diabetes unaware of their condition were less likely to report problems in mobility, self-care, pain/discomfort and usual activities. Conclusion The findings emphasised the need for targeted interventions for high-risk groups, especially individuals with low socio-economic levels.
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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 | 0.000 | 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".