Neuropsychiatric Consequences of COVID-19 Pandemic: A Synthetic Review from a Global Perspective
Bibliographic record
Abstract
Some research suggests that distress, secondary to isolation and fear following COVID-19 infection, can negatively affect the long-term more than the COVID-19 infection itself. This narrative review aims to provide a global view on the neuropsychiatric consequences of COVID-19 that can be ascribed to several factors, ranging from the direct effect of infection, to the body's responses against the infection, or to the psychological sequelae of social isolation, unemployment, and fear for one's health and livelihood. Current findings show that the more severe the respiratory infection, the more likely are central nervous system (CNS) complications regarding the infection itself. The immune reactions to the infection may result in symptoms similar to chronic fatigue as well as neurocognitive deficits, which last long after the infection is gone. An increase in symptoms of depression, anxiety, and trauma-related stress may also follow upon economic fears and isolation from friends and family. The consequences of the pandemic are not limited to adults; children learning remotely and away from classmates and routine activities may develop adjustment disorders, acute stress disorder, and a variety of manifestations of grief. A summary of case reports suggests that COVID-19-related stress, economic recession, and political unrest increase the risk of suicidal behaviors and acts of violence. However, it is unknown whether manifestations of mental disorders result from social causes or whether CNS complications may be responsible.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".