Addressing infodemic for pandemic preparedness in the digital age: a focus on Middle Africa
Bibliographic record
Abstract
Background: The 21st century has brought about a damaging information crisis, significantly challenging and undermining efforts to increase the uptake of scientific research evidence in both policy and practice. The World Health Organization (WHO) recognizes misinformation and disinformation as major drivers of pandemic spread and impact, dedicating a policy brief to pandemic preparedness on this issue. In this study, we examine the impact of mis/disinformation on the use of research evidence in public policy decision-making in West and Central Africa and reflect on how this can inform future pandemic preparedness. Objectives: What factors affect the uptake of scientific evidence during disease outbreaks in Africa? Methods: We used the JBI Scoping Review and Prevalence/Incidence Review methodologies to synthesize the best available evidence. A DELPHI survey was conducted in two stages: the first gathered experiences from policymakers, practitioners, and citizens in Cameroon, Nigeria, and Senegal regarding mis/disinformation and its impact. The second stage explored potential situations related to the issues identified in the first stage. Qualitative data analysis was conducted using MAXQDA. Results: = 4) of infodemic on policy design, implementation, and uptake. Online platforms were identified as the main source of infodemic in 53.3% of cases, compared to 46.7% attributed to offline platforms. We conclude that the severity of COVID-19 as a global pandemic has highlighted the dangers of mis/disinformation, with a considerable number of studies from Middle Africa demonstrating a significant negative impact on the uptake of health policies and to an extend evidence informed policy making. It is also imperative to consider addressing evidence hesitancy in citizens through innovative and indigenous approaches like storytelling. Discussions: Digital technologies, especially social media, play a key role in the propagation of infodemics. For future pandemic preparedness, stakeholders must consider using digital tools and platforms to prevent and mitigate pandemics. This study adds new evidence to the existing body of evidence, emphasizing the need to address infodemics within the context of future pandemic preparedness in Middle Africa.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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".