COVID-19 Risk Perception and its Related Factors and Outcomes in Vulnerable Groups: A Systematic Review
Bibliographic record
Abstract
Background: The COVID-19 disease has worse outcomes in individuals with underlying diseases and elderly individuals. Therefore, identifying COVID-19 risk perception and its related factors and outcomes in vulnerable groups is essential for the health system. Objectives: This study aimed to determine COVID-19 risk perception, its related factors, and outcomes in vulnerable groups (individuals with underlying diseases, smokers, opioid addicts, the elderly, and pregnant women). Methods: This systematic review was conducted based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search was carried out using the keywords “Risk perception” and “COVID-19” in PubMed, Scopus, Science Direct, SID, Proquest, and Magiran databases in the period from 2019 to July 3, 2021. The quality of selected studies was checked by two authors independently according to Newcastle-Ottawa Scale adapted for cross-sectional. Results: In the initial search, 640 articles were found, of which 56 remained in the screening phase. Then, the full text of 56 articles was studied. Eventually, based on the inclusion and exclusion criteria of the articles, 8 articles were reviewed. This systematic review showed that suffering from an underlying disease, more anxiety, younger age, and female gender are associated with higher COVID-19 risk perception. The outcomes of COVID-19 risk perception were higher COVID-19 risk perception, delayed treatment sessions, increased anxiety and fear, increased ineffective safety behaviors, and greater compliance with health protocols. Conclusion: Creating sensitivity and proper COVID-19 risk perception is necessary to follow health protocols, but high COVID-19 risk perception can endanger vulnerable groups’ mental and physical health. Besides, reducing the sensitivity of vulnerable groups toward COVID-19 can expose them to the disease.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".