Psychometrics of the Korean Version of the screen for adult anxiety related disorders (SCAARED)
Bibliographic record
Abstract
Abstract Background For enhanced management of anxiety disorders, early screening and accurate diagnostic differentiation are essential. The Screen for Adult Anxiety Related Disorders (SCAARED) has been developed to identify and categorize anxiety disorders, thereby facilitating timely and appropriate interventions. In line with this, we aimed to translate and validate the Korean version of the SCAARED questionnaire for the Korean population. Methods The original SCAARED was translated into Korean and administered to community adult population (N = 119) ages 18–45 years old in South Korea. The internal consistency and test-retest reliability of the SCAARED were evaluated. In addition, its factor structure was examined using confirmatory and exploratory factor analysis. Concurrent validity was evaluated by comparing SCAARED with the Depression, Anxiety and Stress Scale-21 (DASS), the Beck’s Anxiety Inventory (BAI) and the State-Trait Anxiety Inventory (STAI). Test-retest reliability was evaluated one week after the first assessment. Results The SCAARED showed good internal consistency (Cronbach’s α = 0.945) and test-retest reliability (γ = 0.883). The SCAARED had significant correlation with DASS-21 subscales (γ = 0.655–0.701), BAI (γ = 0.788) and STAI subscales (γ = 0.548–0.736), confirming good concurrent validity. The results of the Exploratory Factor Analysis showed four factors comparable to the original SCAARED (Generalized anxiety, Somatic/Panic/Agoraphobia, Social anxiety, and Separation anxiety). The area under the curve of the receiver operating characteristic of total and each of the factor scores ranged from 0.724 to 0.942. Conclusions The Korean version of the SCAARED is a reliable and valid instrument to screen for anxiety disorders in the Korean adult populations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".