COVID-19 stress syndrome in the German general population: Validation of a German version of the COVID Stress Scales
Bibliographic record
Abstract
The COVID Stress Scales (CSS) are a new self-report instrument for multidimensional assessment of psychological stress in the context of the pandemic. The CSS have now been translated and validated in over 20 languages, but a validated German version has not yet been available. Therefore, the aim was to develop a German version of the CSS, to test its factor structure, reliability, and validity, and to compare it with international studies. In an online survey (08/2020-06/2021), N = 1774 individuals from the German general population (71.5% female; Mage = 41.2 years, SD = 14.2) completed the CSS as well as questionnaires on related constructs and psychopathology. After eight weeks, participants were asked to participate again for the purpose of calculating retest reliability (N = 806). For the German version, the 6-factor structure with good model fit (Root Mean Square Error of Approximation, RMSEA = 0.06) was confirmed, with the six subscales: Danger, Socio-Economic Consequences, Xenophobia, Contamination, Traumatic Stress, and Compulsive Checking. Internal consistencies ranged from ω = .82-.94 (except Compulsive Checking ω = .70), and retest reliability from rtt = .62-.82. Convergent and discriminant validity were confirmed for the German version. Related constructs such as health anxiety, general xenophobia, obsessive-compulsive behavior, and posttraumatic stress disorder symptoms correlated moderately with the respective subscale and lower with the other scales. With anxiety and depression, Traumatic Stress showed the strongest correlation. Overall, there was a high degree of agreement in an international comparison. The CSS can help to identify pandemic-related psychological stress and to derive appropriate interventions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".