Has the COVID-19 Pandemic Traumatized Us Collectively? The Impact of the COVID-19 Pandemic on Mental Health and Sleep Factors via Traumatization: A Multinational Survey
Bibliographic record
Abstract
Purpose: The COVID-19 pandemic affects mental health and sleep, resulting in frequent nightmares. Therefore, identifying factors associated with nightmare frequency is important, as it can indicate mental health issues. The study aimed to investigate increases in nightmare frequency comparing the pre-pandemic and pandemic period, and identify its risk factors. Further, the mediating role of post-traumatic stress disorder symptoms between the pandemic and nightmares is explored. Patients and Methods: For this cross-sectional survey data were obtained via self-rating online survey (ICOSS: details in Partinen et al, 2021), which was open to anyone older than 18 years. The final volunteer sample consisted of 15,292 participants, divided according to their nightmare frequency (high: ≥1-2 nights/week; low: <1-2 nights/week). A total of 9100 participants were excluded if answers on variables of interest were missing or receiving rewards for participation. Chi-square tests identified changes of nightmare frequency. Predictors of high nightmare frequency were assessed using logistic regression and presented as Odds Ratios. Post-hoc mediation models were used to investigate the role of post-traumatic stress symptoms (PTSS). Results: The mean age was 41.63 (SD=16.55) with 64.05% females. High nightmare frequency increased significantly from 13.24% to 22.35% during the pandemic. Factors associated with it included self-reported PTSS (OR=2.11), other mental disorders and various sleep disorders or problems. Financial burden due to the pandemic, confinement, having had COVID-19, and work situation during the pandemic were associated with nightmare frequency, those relations were partly mediated through PTSS. Conclusion: Our results display the pandemic influence on nightmare frequency, which in turn connects to multiple mental health and sleep factors. These relations were partly mediated through PTSS. The COVID-19 pandemic appears to have caused traumatization of a substantial proportion of society. Health care workers should consider nightmares in their screening routines, as it might indicate PTSS and/or other mental and sleep disorders.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".