COVID-19, Conspiracy Theories and Vaccine Inequity – The Nigerian Perspective
Bibliographic record
Abstract
Background The World Health Organization in 2020 declared that, alongside the COVID-19 pandemic, it was also fighting an INFODEMIC— an overabundance of information, both online and offline. The viral information included conspiracy theories about the origins of the virus and vaccines. In Nigeria, lack of trust in the government accelerated the belief in these theories. Trust is often linked to past experiences, like the Pfizer drug trial where 11 children died, and some disabled. Conspiracy theories should not be regarded as baseless, false beliefs but as expressions of fear in times of uncertainty and can influence decision-making, like vaccine uptake. Apart from vaccine hesitancy in Africa, failures in vaccine equity, with distribution skewed towards higher-income countries, is a problem that needs attention. Vaccine inequity has increased mistrust among Africans and risks dividing vaccinated and unvaccinated countries into historical haves and have-nots, which some authors describe as modern-day colonialism in Global health. Aim This study explored conspiracy theories about COVID 19, their sources, and their impact on pandemic control measures like vaccination, amongst pregnant women and their health care providers in Nigeria. Method Twenty semi-structured in-depth interviews and observations of pregnant women and health care workers were done in North-central Nigeria. Participant selection was purposive, and the data was analyzed by Thematic analysis using an inductive coding approach. Findings There was widespread mistrust in the government and public health system. The pandemic influenced social media use and vice versa. There was a high prevalence of conspiracy theories. Top amongst them were that COVID did not exist, data were exaggerated, and the vaccine was a tool for higher-income countries to reduce the African population, alter their DNA, control, and track them. For Christians, the pandemic was a sign of end-times predicted in the scriptures. Conclusion Public health measures should tackle conspiracy theories by providing better communication channels, developing strategies to address ignorance, religious fanaticism, social media censoring, and providing counter information to rebuild trust amongst Nigerians. Vaccine inequity should be addressed by reviewing vaccine production, distribution and trade regulations; and avoiding donor-driven models where poorer countries are gifted vaccine leftovers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 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".