Emerging Mental Health Issues from the Novel Coronavirus (COVID-19) Pandemic
Bibliographic record
Abstract
The unprecedented widespread pandemic of the novel coronavirus (COVID-19) has continued to have a tremendous impact on nations around the world. Government controls and restrictions were put in place and are currently being updated to increase social isolation and social (physical) distancing to slow the spread of the virus. As a result, it is expected that there will be unparalleled psychological distress impacting individuals at a global level. Given that the COVID-19 pandemic is expected to continue for the coming months with the possibility of multiple waves, it is imperative to understand the magnitude of mental health issues that will arise during and after this public health crisis. A review of existing literature was assessed to understand the mental health issues that emerge during a pandemic. MEDLINE, Pubmed, APA PsycInfo & CINAHL Plus were reviewed to identify articles published from 2000 to 2020. Of the 203 unique articles reviewed, 16 articles were included in this study. From these articles, important mental health themes identified were related to social isolation, social (physical) distancing, quarantine, caregiver stress, unemployment, and death/illness. The impact on frontline workers and those suffering from mental health disorders are also important factors during this pandemic. These themes provide important areas for mental health strategies and policies which will ultimately impact the burden of mental health in the months to come.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".