Interventions to Mitigate COVID-19 Misinformation: A Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it