Translation, validation and reliability of depression, anxiety, and stress 42 (DASS-42) among Herat University students
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the validity and reliability of the Dari-translated Depression, Anxiety, and Stress Scale-42 (DASS-42) in university students in Herat, Afghanistan. METHODS: A cross-sectional study was conducted from August to November 2022 with 1000 undergraduate students at Herat University. Participants completed a self-reported Dari-translated DASS-42 questionnaire. Confirmatory Factor Analysis (CFA) was employed to assess the scale's factor structure, and internal consistency was evaluated using Cronbach's alpha. The test-retest reliability was also examined over a 2-3 week interval. RESULTS: The CFA results indicated a good fit for the three-factor model, with RMSEA = 0.04 and CFI = 0.919, confirming the construct validity of the scale. Internal consistency was high, with Cronbach's alpha values of 0.867, 0.831, and 0.848 for depression, anxiety, and stress, respectively. The test-retest reliability coefficients for depression, anxiety, and stress were 0.956, 0.923, and 0.951, respectively. Additionally, DASS-42 showed significant correlations with other established mental health measures, indicating good convergent validity. CONCLUSION: The DASS-42 is a valid and reliable instrument for assessing depression, anxiety, and stress among university students in Herat, Brazil. This comprehensive validation addresses previous limitations and supports the use of the scale in both clinical and non-clinical settings for early detection and intervention. Future research should explore the scale's applicability in more diverse Afghan populations and incorporate longitudinal designs to validate its predictive validity further.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".