Interventions to Mitigate COVID-19 Misinformation: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The duration and impact of the COVID-19 pandemic depends largely on individual and societal actions which are influenced by the quality and salience of the information to which they are exposed. Unfortunately, COVID-19 misinformation has proliferated. Despite growing attempts to mitigate COVID-19 misinformation, there is still uncertainty regarding the best way to ameliorate the impact of COVID-19 misinformation. To address this gap, the current study uses a meta-analysis to evaluate the relative impact of interventions designed to mitigate COVID-19-related misinformation. We searched multiple databases and gray literature from January 2020 to September 2021. The primary outcome was COVID-19 misinformation belief. We examined study quality and meta-analysis was used to pool data with similar interventions and outcomes. 16 studies were analyzed in the meta-analysis, including data from 33378 individuals. The mean effect size of interventions to mitigate COVID-19 misinformation was positive, but not statistically significant [d = 2.018, 95% CI (−0.14, 4.18), p = .065, k = 16]. We found evidence of publication bias. Interventions were more effective in cases where participants were involved with the topic, and where text-only mitigation was used. The limited focus on non-U.S. studies and marginalized populations is concerning given the greater COVID-19 mortality burden on vulnerable communities globally. The findings of this meta-analysis describe the current state of the literature and prescribe specific recommendations to better address the proliferation of COVID-19 misinformation, providing insights helpful to mitigating pandemic outcomes.
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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 | 0.024 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".