Validation of depression, anxiety and stress scales (DASS-21): Immediate psychological responses of students in the e-learning environment
Bibliographic record
Abstract
The COVID-19 epidemic has caused higher education institutions in Vietnam to immediately transfer from traditional classrooms to e-learning environments. This interacts with high expectations and habits of learning and training can adversely affect the mental health of students. The purpose of this study is to validate the DASS-21 scale for use in the mental health screening in Vietnamese students when they suffer an immediate psychological reaction in the e-learning environment. Strict statistical analyzes (including Cronbach's alpha, Exploratory Factor Analysis, Confirmatory Factor Analysis, Average Variance Extracted, Average Shared Variance) have led to a well-fitting model of DASS-18 with a three-factor structure to measure the mental health of Vietnamese students in an e-learning environment. Results DASS-18 reported the rates of depression, anxiety, and stress in levels of moderate severity or above in Vietnamese students at 50%, 19.7%, and 37.3%, respectively. However, a rate of anxiety up to 43.1% by using DASS-21 indicating that many students may be misdiagnosed for the level of anxiety. Finally, linear regression analyses are used to examine the influence of socio-demographic factors on the immediate psychological responses of students to an e-learning environment in the context of the COVID-19 epidemic.
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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 | 0.003 | 0.007 |
| 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 | 0.000 |
| 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 | 0.000 |
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".