Factors Associated with COVID-19 Low Risk perception among urban dwellers in Malawi
Bibliographic record
Abstract
Abstract Introduction: Malawians have yet to broadly adopt COVID-19 mitigation measures despite having overwhelming evidence about its infectivity, morbidity, and fatality. Understanding why the general population is not proactive in reducing the spread of this illness is critical to learning how to address this issue. This study explores Malawian COVID-19 risk perception and the associated constraints in the adoption of mitigations. A Health Belief Model (HBM) approach was used to understand factors that undermine COVID-19 messages to achieve behavior change.Methods: The study applied a rapid appraisal and photovoice methodology in this qualitative inquiry to better understand individual risk perception regarding COVID-19. We selected 33 participants from three major cities in Malawi. We transcribed verbatim audio interviews and videos. Transcripts were coded manually to derive key themes and concepts. Results: Religious and political beliefs strongly influenced COVID-19 risk perception. Critical religious factors included the coming of Christ, the wrath of God, and the coming of the beast. Politically, participants believed that COVID-19 lockdown measures were a ploy by the 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 on the Malawi populous.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".