Depression among Bangladeshi diabetic patients: a cross-sectional, systematic review, and meta-analysis study
Bibliographic record
Abstract
AIM: This study aims to assess the prevalence and associated factors of depression among diabetic patients in a cross-sectional sample and perform a systematic review and meta-analysis of the extant studies to date. METHODS: A face-to-face semi-structured interview of established diabetic patients was conducted in four districts of Bangladesh between May 24 to June 24, 2022, and the Patient Health Questionnaire (PHQ-2) was used to detect depression. PRISMA guidelines were followed to conduct a systematic review and meta-analysis, with Bangladeshi articles published until 3rd February 2023. RESULTS: The prevalence of depression among 390 diabetic patients was 25.9%. Having secondary education and using both insulin and medication increased the likelihood of depression, whereas being a business professional and being physically active reduced the likelihood of depression. The systematic review and meta-analysis indicated that the pooled estimated prevalence of depression was 42% (95% CI 32-52%). Females had a 1.12-times higher risk of depression than males (OR = 1.12, 95% CI: 0.99 to 1.25, p < 0.001). CONCLUSIONS: Two-fifths of diabetic patients were depressed, with females at higher risk. Since depression among diabetic patients increases adverse outcomes, improved awareness and screening methods should be implemented to detect and treat depression in diabetic patients.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.016 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".