Social stigma during COVID-19: A systematic review
Bibliographic record
Abstract
Objectives: Stigmatization was reported throughout the COVID pandemic for COVID-19 patients and close contacts. The aim of this systematic review was to comprehensively examine the prevalence and impact of stigmatization during COVID-19 pandemic. Methods: English articles were searched using online databases that included PubMed, Scopus, Embase, and Web of Science up to 24 August 2022. A two-step screening and selection process was followed utilizing an inclusion and exclusion criteria and then data was extracted from eligible articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was followed, and the risk of bias was assessed using the Newcastle-Ottawa Scale. Results: Seventy-six studies were eligible for inclusion. Twenty-two studies reported the prevalence of social stigma due to COVID-19 infection with social isolation being the most commonly reported stigma. There were 20 studies that reported the majority of participants experienced stigma due to COVID-19 infection, which was as high as 100% of participants in two studies. Participants in 16 studies reported blaming from others as the second most common type of stigma, with various other types reported such as psychological pressure, verbal violence, avoidance, and labeling. The most common effect of the stigma was anxiety followed by depression, and then reduction of socialization. Conclusion: Findings from the present review have identified that COVID-19-related stigma studies have generally focused on its prevalence, type, and outcome. Greater awareness of this topic may assist with improving public education during pandemics such as COVID-19 as well as access to support services for individuals impacted by stigmatization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".