DASS-21: assessment of psychological distress through the Bifactor Model and item analysis
Bibliographic record
Abstract
Abstract The term distress has been used to refer to a continuous variable operationalized through symptoms of depression, anxiety, and stress. In this study, psychological distress is measured using the Depression, Anxiety, and Stress Scale (DASS-21). Confirmatory Factor Analysis compared the fit of different measurement models for the DASS-21, with the parameters of the items verified through the Andrich rating scale model. A non-clinical sample of 530 participants (mean age=24.35±6.55 years; 71.89% women) responded to the instrument. According to the theoretical hypothesis, the results indicated a better fit for the bifactor model, composed of three specific factors (depression, anxiety, and stress) and a general factor (general psychological distress). The assessment of the item properties allowed for a better understanding of the organization of the continuum represented by the construct psychological distress. It is possible to conclude that the Brazilian version of the DASS-21 is an adequate measure for psychological distress.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".