Misinformation and Lack of Evidence-Based Communication during the COVID-19 Pandemic: A Narrative Review.
Bibliographic record
Abstract
Social media and online communication are integral in how societies consume information today. As social media platforms (Facebook, Twitter, Instagram, and YouTube) support the spread and reach of information to over a billion users worldwide, health promotion strategies aim to improve the public’s ability to obtain accurate online health content. The current COVID-19 pandemic is paralleled by an overabundance of information, making it difficult to determine what is valid and credible and what is false. Misinformation surrounding prevention measures and cures can lead to negative public health effects. The WHO is working collaboratively with social media platforms to track and respond to misinformation by validating evidence-based facts, providing warning labels on inaccurate content, and removing posts that make false claims to reduce the spread of misinformation related to COVID-19. This narrative review aims to summarize the effects of social media during a public health emergency and explore the current and newly implemented policies that aim to limit the spread of misinformation during the COVID-19 pandemic. We will examine the addition of health literacy initiatives through multimedia campaigns to strengthen community action and develop personal skills, as identified in the Ottawa Charter for Health Promotion, to react and reflect on information regarding the COVID-19 pandemic. We highlight a gap in the literature related to information sharing and consumption and provide recommendations for promoting public health literacy and improving the online presence of public health professionals.
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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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| 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".