Using black knowledges to recognise and address barriers to COVID-19 vaccination in Malawi
Bibliographic record
Abstract
BACKGROUND: A Black Geographies framework offers a perspective through which the rich and complex histories and knowledges of African nations, and the people themselves, can be explored to reveal barriers to vaccination and solutions to achieving global vaccine access and equity. This research centres Malawi as a case study and seeks to (1) identify barriers to COVID-19 vaccination; (2) situate these barriers within geographic scales, ranging from the local to the global; and, (3) identify the role of the (Black) individual in creating, perpetuating, navigating and overcoming these barriers. METHODS: The study employed a qualitative approach, using in-depth semi-structured interviews with 41 key informants in Malawi between September and December 2021 to explore barriers to vaccination. Recruitment focused on ensuring diverse perspectives based on occupation, location, gender, and ethnicity. Among the informants, 26 were men and 15 were women; 31 lived in rural areas and 10 in urban areas; 21 had completed secondary school, while 20 had not. Twenty interviews were conducted in English, while 21 were in either Chitumbuka or Chichewa. Interview transcripts were analysed using the framework method. RESULTS: Results reveal that the fear of COVID-19 in Malawi was largely tied to disruptions in cultural practices like burials, creating anxiety about “meaningless deaths” without proper rites. This fear, rooted in the community’s lifeworlds, contributed to vaccine hesitancy, compounded by a history of colonial exploitation and racial mistrust, with some believing that the vaccine was designed to harm them (Black peoples and/or Africans). Structural barriers such as vaccine nationalism and logistics posed challenges in Malawi, further hindering access. In response, Malawi’s government developed culturally specific public health strategies, leveraging traditional and social media, community leaders, and a dual approach that combined Western and traditional medicine to promote vaccination. This Malawian approach emphasises the importance of acknowledging local knowledges, cultural practices, and Black spatial agency in navigating and addressing vaccine uptake. CONCLUSIONS: This study underscores the critical importance of integrating Black knowledges and voices into vaccine rollout and distribution policies. Through the lens provided by the Black Geographies framework, we highlight the analytical strength of Blackness and Black knowledges. The study identifies barriers to vaccination in Black communities and proposes solutions rooted in these perspectives. Furthermore, it emphasises the need to acknowledge present-day global power dynamics in vaccine messaging and distribution to ensure equitable access and benefits for populations worldwide.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".