Psychological Burden During the COVID-19 Pandemic in Germany
Bibliographic record
Abstract
After the first COVID-19 case was diagnosed in Germany, various measures limiting contact between people were introduced across the country. The implementation of these measures varied between jurisdictions and potentially had a negative impact on the psychological well-being of many people. However, the prevalence, severity, and type of symptoms of psychological burden has not been documented in detail. In the current study, we analysed various self-reported symptoms of psychological burden in a German sample. The dataset was collected between April 8th and June 1st, 2020, through an online survey measuring psychological burden using the ICD-10-symptom rating scale. More than 2,000 individuals responded to the survey, with a total of 1,459 complete datasets. Data was then sampled to compare (1) the new data to an existing demographically comparable reference dataset including a total of 2,512 participants who did not undergo any kind of contact restrictions or other pandemic measurements, and (2) psychological burden in two different German states. In line with recent observations from Germany, Italy, China, Austria and Turkey, we found a high prevalence of depressive symptoms in comparison to the reference sample. Furthermore, we found a high prevalence of eating disorder and compulsion symptoms. Especially younger adults and women reported a higher symptom severity compared to other groups during our measurement period. However, no difference between the two states in psychological burden was found.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".