Impact of strategies to mitigate misinformation in diverse settings and populations: a protocol for a living evidence synthesis
Bibliographic record
Abstract
INTRODUCTION: Misinformation refers to inadvertent misleading information that the public may be exposed and share without intent to cause harm, and can delay or prevent effective care, affect mental health, lead to misallocation of health resources and/or create or exacerbate public-health crises. There are many strategies to address misinformation, but there is a need to evaluate their effects. Our objective is to synthesise and routinely update evidence to assess the impact of strategies to mitigate health-related misinformation in diverse settings, and populations. METHODS AND ANALYSIS: We will search seven databases in May 2023 with planned updates at 6 and 9 months, which will be supplemented with searches for grey literature and reference lists of included studies and contacting experts. Two reviewers will independently screen all search results for studies that evaluate one or more approaches to addressing health-related misinformation. One researcher will conduct data extraction and risk of bias assessments, which will be reviewed by a second reviewer for accuracy. We will include experimental, quasi-experimental and observational studies for any populations, settings and diseases without language or publication restrictions. We will conduct quantitative analysis if meta-analytical pooling is possible. If pooling is not possible, we will synthesise quantitative data according to outcomes and interventions addressed, and present a narrative summary of findings disaggregated by sex and/or gender, irrespective of whether differences were found. ETHICS AND DISSEMINATION: There are no individuals or protected health information involved and no safety issues identified. Results will be published through the Global Commission on Evidence and COVID-END websites, in a peer-reviewed journal, as well as through plain-language materials. PROSPERO REGISTRATION NUMBER: CRD42023421149.
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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 | 0.210 | 0.285 |
| Meta-epidemiology (narrow) | 0.008 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.019 | 0.013 |
| Insufficient payload (model declined to judge) | 0.091 | 0.023 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".