Mental Health Concerns of Frontline Workers During the COVID-19 Pandemic: A Scoping Review
Bibliographic record
Abstract
OBJECTIVES: The current COVID-19 pandemic continues to have a significant impact on the mental health of frontline workers worldwide. Currently there are limited published studies addressing mental health issues in frontline workers. The objective of this scoping review is to examine the range of existing global literature on mental health issues reported in frontline workers during the COVID-19 pandemic and to understand what mitigating factors exist. METHODS: The scoping review was guided by the Levac Colquhoun and O’Brien’s adapted version of Arkey and O’Malley’s framework. We performed a comprehensive search of three databases, Pubmed, APA PsychINFO, and CINAHL, identifying 684 studies. In total, 16 original studies and 4 letters to editors were included in this review. RESULTS: Of the original studies, 13 were published in China, and the remaining 3 in Italy, Turkey, and Iraq; all letters to editors were published in China. Sources of stress reported in frontline workers across studies included direct contact with COVID-19 patients, isolation, putting loved ones at risk, facing life and death decision making with COVID-19 patients, uncertainty with COVID-19 disease control, limited personal protective equipment, time spent thinking about COVID-19, limited staff/resources/pay, burnout, and stigma. Mental health symptoms and outcomes reported in frontline workers were fear, stress, anxiety, depression, insomnia, burnout, and psychological distress. CONCLUSION: Findings demonstrate the immediate need to increase mental health awareness and resources at an individual and system wide level. Mental health programs need to be catered towards each unique workplace to provide the necessary resources for frontline workers.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".