Strategies to address conspiracy beliefs and misinformation on COVID-19 in South Africa: A narrative literature review
Bibliographic record
Abstract
Conspiracy theories and misinformation have been explored extensively however, strategies to minimise their impact in the context of coronavirus disease 2019 (COVID-19) vaccines are limited. This study aimed to explore strategies that can be used to reduce the negative effects of conspiracies and misinformation about SARS-CoV-2. This review was carried out based on accessed literature on beliefs in misinformation about the COVID-19 pandemic. A comprehensive search of databases, such as Google Scholar, EBSCOhost and African Journals between 2019 and 2022 yielded qualitative and quantitative studies. Two themes emerged, namely underlying motives for conspiracy theories and belief in misinformation about the pandemic and ways to overcome them. The latter included: (1) strengthening critical scanning of information, (2) critical review to address misinformation and (3) establishing approaches for managing conspiracy theories. A proposal is made to address conspiracy beliefs about COVID-19 infection. Contribution: This is believed to be the first review that describes strategies to mitigate belief in conspiracies and misinformation to promote vaccination.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".