Development of a Conceptual Framework of Health Misinformation During the COVID-19 Pandemic: Systematic Review of Reviews
Bibliographic record
Abstract
BACKGROUND: Despite the wide variety of studies that have focused on the recent COVID-19 infodemic, defining health mis- or disinformation remains a challenge due to the dynamic nature of the social media ecosystem and, in particular, the different terminologies from different fields of knowledge. OBJECTIVE: In this work, we aim to develop a conceptual framework of health misinformation during pandemic contexts that will enable the establishment of an interoperable definition of this concept and consequently a better management of these problems in the future. METHODS: We conducted a systematic review of reviews to develop a conceptual framework for health misinformation during the pandemic context as a case study. RESULTS: This review comprises 51 reviews from which we developed a conceptual framework that integrates 6 key domains-sources, drivers, content, dissemination channels, target audiences, and health-related effects of mis- or disinformation-offering a structured approach to analyze and categorize health misinformation. These 6 domains collectively form the basis of our proposed conceptual framework. CONCLUSIONS: Our results highlight the complexity and multifaceted nature of health disinformation and underscore the need for a common language across disciplines addressing this global problem in order to use interoperable definitions and advance this evolving field of study. By offering a structured conceptual framework, we also provide a valuable foundation for interventions aimed at surveillance, public communication, and digital content moderation in future health emergencies.
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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 | 0.061 | 0.166 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.046 | 0.031 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".