Religion, politics and COVID-19 risk perception among urban residents in Malawi
Bibliographic record
Abstract
INTRODUCTION: Majority of Malawians have not yet adopted COVID-19 mitigation measures despite having knowledge about its infectivity, morbidity, and fatality. Understanding drivers of hesitancy to adoption of COVID-19 mitigation measures is critical as it can inform prevention programs. This study explores Malawians' COVID-19 risk perception, and the associated constraints in the adoption of mitigation efforts. A Health Belief Model (HBM) approach was used to understand perceived factors that undermine public health COVID-19 messages to reduce the spread of the pandemic in Malawi. METHODS: The study applied rapid appraisal and photovoice qualitative inquiry to comprehend risk perception regarding COVID-19. We purposively selected 52 participants from three major cities in Malawi. Audio and video interviews were transcribed verbatim, and transcripts were coded manually to derive key themes and concepts. RESULTS: The study identified that social factors particularly religious and political beliefs influenced COVID-19 risk perception. Specific religious beliefs pertaining to individuals recognizing signs of the 'Christian apocalypse' were particularly associated with lower risk perceptions. Politically, participants believed COVID-19 lockdown measures were a ploy by the then-ruling party to remain in power. CONCLUSION: The study suggests that religious beliefs and political environment undermine self -perceived risk of contracting COVID-19 among urban dwellers in Malawi. We recommend that diverse actors in Malawi should collaborate to promote the dissemination of accurate COVID-19 discourses and reduce the severity of the pandemic's impact in Malawi.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".