Factors Associated with Acute and Persistent Psychological Disturbances During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract Background: The emergence of Corona virus disease (COVID-19) and wide-spread counter-measures, such as quarantine and social distancing, can have a significant impact on mental health of individuals. Methods: This online study anonymously screened 13,332 individuals worldwide for acute psychological symptoms related to Corona virus disease 2019 (COVID-19) pandemic from March 29 th to April 14 th , 2020. A total of n=12,817 responses were considered valid. n=1077 participants from Europe were screened a second time during May 15 th to May 30 th to longitudinally ascertain the persistence of psychological effects. Results: Female gender, pre-existing psychiatric condition, and prior exposure to trauma were identified as notable factors associated with increased acute psychological symptoms during COVID-19. The same factors, in addition to, being related to someone who demised due to COVID-19 and using social media more than usual predicted persistence of psychological disturbances. Optimism, ability to share concerns with family and friends like usual, positive prediction about COVID-19, and daily exercise predicted fewer acute and persistent psychological symptoms. Conclusions: Females, psychiatric patients, and individuals with previous exposure to trauma are at increased risk of being persistently psychologically affected by COVID-19.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".