Frontline Healthcare workers suffering from psychosomatic disorders during COVID-19 (a pandemic) – A Systematic review
Bibliographic record
Abstract
Abstract Purpose The emergence of SARS CoV-2, has imposed high pressure on the healthcare system worldwide. As a consequence, frontline healthcare workers were impacted widely. The aim of this systematic review is to examine the impact of COVID-19 on mental status of FHW during pandemic. Methods Databases such as PubMed, Scopus, google scholar were searched extensively from the date of inception till April 2021. All cross-sectional studies published in English assessing the mental condition and well-being of frontline caregivers during COVID-19 were included in the study. The quality assessment was done by Newcastle Ottawa scale. Results Ten thousand eight hundred sixty-nine articles were found. After conscientious literature search, total 78 articles were included satisfying the objective of the review. The highest and lowest values for the rates of depression, anxiety and insomnia was found to be 99.51% & 6.07%, 85.7% & 73.6%, and 5.3% & 11.4%, respectively. Conclusion It has been found that FHW were psychologically impacted by the pandemic. This could be due to lack of resources such as PPE, organizational support, inefficient relevant knowledge regarding the novel virus, its extremely indelible transmission rates, fear of contamination, stigmatization, and/or due to prevalence of ignorance by government and health policy makers. Prospero registration no- CRD42021244612
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".