A Systematic Review of the Impact of Viral Respiratory Epidemics on Mental Health: An Implication on the Coronavirus Disease 2019 Pandemic
Bibliographic record
Abstract
Background: The twenty-first century viral respiratory epidemics have taught us valuable lessons. Our systematic review examined the impact of these epidemics, including coronavirus disease 2019 (COVID-19), on mental health among different population groups, drawing on their insights for recommendations for the current COVID-19 pandemic. Methods: Searches were performed on PubMed, Embase, PsycINFO, Web of Science, Scopus, CINAHL, and Cochrane on April 4, 2020. Studies that had undefined mental health outcomes or did not use a validated scale for measure were excluded. Quality assessment was carried out via the Newcastle–Ottawa Scale. Results: We included 95 studies, most of which were conducted in Hong Kong (31.6%) and China (21.4%). A total of 30 (30.9%) studies are on the general public, 41 (42.2%) on healthcare workers, and 26 (26.6%) on patients and quarantined individuals. Furthermore, 36 (37.1%) of the studies are of high quality, 48 (49.5%) are of moderate quality, and 13 (13.4%) are of low quality. The most significant mental health outcomes reported include anxiety, depression, and post-traumatic stress disorder symptoms. The subgroups identified to have a higher risk of psychiatric symptoms among the general public include females, the elderly, individuals with chronic illness, migrant workers, and students. Long-term mental health impact was reported in some healthcare workers and epidemic patients, even up to 3 years in the former. Interestingly, when compared to non-quarantined groups, quarantine was not significantly associated with worse mental health outcomes. Conclusion: Important implications for the COVID-19 pandemic were highlighted. Respiratory epidemics pose a significant psychological morbidity onto many population groups. Psychological support for vulnerable groups, including healthcare workers and patients, should be implemented to prevent them from spiraling into clinical psychiatric conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".