Depression and suicidal ideation among individuals with type-2 diabetes mellitus, a cross-sectional study from an urban slum area of Karachi, Pakistan
Bibliographic record
Abstract
Background: Suicidal thoughts and depression are associated with patients with diabetes, especially patients with low socioeconomic backgrounds and prolonged illness. Objective: We aimed to estimate suicidal thoughts and depression among patients with type 2 diabetes (T2D) in the slums of Karachi. Methods: This cross-sectional study was conducted across 38 locations in the slums of Karachi to understand depression, suicidal thoughts, and other supporting factors of depression associated with T2D. The three-item Oslo Social Support Scale, the Patient Health Questionnaire-9 (PHQ-9) scale, and the Ask Suicide Screening Questions were used to screen the patients. Results: A total of 504 study participants were interviewed, with a response rate of 98%. The prevalence of depression among patients with diabetes was 30.83%, and suicidal ideation was 20.39%. In the final multivariate analysis, being socioeconomically poor, physically disabled, and having poor social support were independent predictors of depression. Conclusion: Diabetes, low socioeconomic level, a lack of social support, and physical disability were all linked to depression. Therefore, trained health providers should conduct an early depression-focused routine screening for patients with diabetes.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".