Implications of social media misinformation on COVID-19 vaccine confidence among pregnant women in Africa
Bibliographic record
Abstract
It has been over a year since the World Health Organization (WHO) declared the outbreak of COVID-19 as a Public Health Emergency of International Concern and subsequently a global pandemic. The world has experienced a lot of uncertainty since then as we all get used to this new 'normal' with social distancing measures, lockdowns, the emergence of new variants, and an array of hope with the development of vaccines. Having an abstract understanding of vaccine delivery, public perceptions of vaccines, and promoting acceptance of vaccines are critical to tackling the pandemic. The advent of the pandemic has led to the emergence of an 'infodemic' or rampant misinformation surrounding the virus, treatment, and vaccines. This poses a critical threat to global health as it has the potential to lead to a public health crisis by exacerbating disease spread and overwhelming healthcare systems. This 'infodemic' has led to rising vaccine hesitancy which is of paramount concern with the WHO even identifying it as one of the ten main threats to Global health almost 2 years before the approval of COVID-19 vaccines. Pregnant African women are one of the most vulnerable population groups in a region with an already burdened healthcare system. Currently, there isn't ample research in the literature that explores vaccine hesitancy in this subpopulation and the impact of social media misinformation surrounding it. The aim of this paper is to highlight the implications of this 'infodemic' on the pregnant African population and suggest key recommendations for improved healthcare strategies.
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How this classification was reachedexpand
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".