COVID-19 risk perception among residents of seven sub-Saharan African countries: socio-demographic correlates and predicted probabilities
Bibliographic record
Abstract
INTRODUCTION: as the COVID-19 pandemic rages on, sub-Saharan Africa remains at high risk given the poor adherence to pandemic control protocols. Misconceptions about the contagion may have given rise to adverse risk behaviours across population groups. This study evaluates risk perception among 2,244 residents of seven countries in sub-Saharan Africa (Botswana, Kenya, Malawi, Nigeria, Tanzania, Zambia and Zimbabwe) in relation to socio-demographic determinants. METHODS: an online survey was conducted via social media platforms to a random sample of participants. Risk perception was evaluated across six domains: loss of income, food scarcity, having a relative infected, civil disorder, criminal attacks, or losing a friend or relative to COVID-19. A multivariable ordinal logistic regression was conducted to assess socio-demographic factors associated with the perceived risk of being affected by COVID-19. RESULTS: 595 (27%) respondents did not consider themselves to be at risk, while 33% perceived themselves to be at high risk of being affected by the pandemic with respect to the six domains evaluated. Hospital-based workers had the highest proportional odds (3.5; 95%CI: 2.3-5.6) high perceived risk. Teenage respondents had the highest predictive probability (54.6%; 95% CI: 36.6-72.7%) of perceiving themselves not to be at risk of being affected by COVID-19, while Zambia residents had the highest predictive probability (40.7%; 95% CI: 34.3-47.0%) for high-risk perception. CONCLUSION: this study reveals the need to increase awareness of risks among socio-demographic groups such as younger people and the unemployed. Targeted risk communication strategies will create better risk consciousness, as well as adherence to safety measures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".