Validity, reliability, and the factorial structure of bangla version depression, anxiety, and stress scale (DASS-21) among Bangladeshi healthcare professionals
Bibliographic record
Abstract
The objective of this study was to assess the validity, reliability, and factorial structure of the Bengali-translated Depression, Anxiety, and Stress Scale—21 (DASS-21) among Bangladeshi health professionals. This study incorporated the cross-sectional study design, conducted among 312 health professionals from 28 health facilities in 18 districts across Bangladesh. The internal consistency and intraclass correlation coefficient of the DASS-21 was assessed. The confirmatory factor analyses was done to assess the fitness of different structural models of DASS-21. The concurrent validity was estimated by using the PHQ-9 and GAD-7 scales. In total, 258 healthcare professionals (82.7%) out of 312 completed the whole survey questionnaire and were included in the study. The internal consistency of the DASS-21 exhibited satisfactory internal consistency (α = 0.93) and ICC = 0.92). The conventional three-factor model appeared to best fit the Bengali-translated DASS-21 scale. The discriminant validity has been affirmed by strong evidence. The convergent validity of Bengali DASS-21 was confirmed by the PHQ-9 and GAD-7 scales. This study found good validity, reliability, and suitability of the DASS-21 used among the Bangladeshi healthcare professionals. We warrant future research to explore the validity of this tool in other cohorts 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".