Mental health during the COVID-19 pandemic: Stress and strain profiles in the German population
Bibliographic record
Abstract
Clinical observations indicate that people frequently display stress-related behavior during the COVID-19 pandemic. Although numerous studies have been published concerning pandemic-related psychological distress, systematic data on the interrelationships between stress sensitivity, personality, and behavioral characteristics of people are still lacking. In the present cross-sectional online survey study, we applied a German version of the COVID Stress Scales (CSS) and standard psychological questionnaires to systematically identify the complex interplay between stress sensitivity, gender, and personality in the modulation of quality of life and mental health in the German population (N = 1774; age ≥ 16 years). A CSS-based cluster analysis revealed two clusters characterized by higher and lower stress levels. Study participants in each cluster differed significantly with respect to neuroticism, extraversion, agreeableness, quality of life, depression, and anxiety. Females were significantly overrepresented in the higher stress cluster, while there was an overrepresentation of males in the lower stress cluster. Neuroticism was identified as a risk factor and extraversion as a protective factor for enhanced pandemic-related stress responses. For the first time our data show a taxonomy of factors, which modulate pandemic-related stress sensitivity and warrant consideration as key indicators of quality of life and psychological distress during the COVID-19 pandemic. We suggest that our data may advise governmental regulation of pandemic-related public health measures, to optimize quality of life and psychological health in different groups of the population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".