Perceptions of COVID-19 risks and protective actions: a qualitative study in six Sudanese communities
Bibliographic record
Abstract
Abstract Background Risk communication is an essential strategy in outbreak response. Understanding perceptions of health risks and protective behaviours is critical for informing effective risk communication during outbreaks. This study aimed to explore Coronavirus disease (COVID-19) knowledge, risk perception and precautionary behaviours during the early weeks of the COVID-19 epidemic in Sudan. Methods In-depth telephone interviews were conducted with 59 adults from six of urban, rural and forcibly displaced communities in Sudan. Participants were from households with members at higher risk of severe COVID-19 outcomes. We analysed data using participatory group analysis followed by a thematic inductive and deductive analysis of interview transcripts. We used the Health Belief Model to analyse, present and discuss the findings. Results Most participants perceived a high susceptibility to COVID-19, especially among older people, due to novelty and transmission characteristics of the disease. However, a few were mainly concerned about the livelihood implications of the government’s response. Our respondents had good knowledge about COVID-19 although there were a few misconceptions. Most participants viewed COVID-19 as a highly infectious, dangerous and fatal disease. Most participants understood the benefits of protective measures and reported complying with hand hygiene. A few reported complying with social and physical distancing, including stay-at-home orders. Compliance was generally poorer among older adults. Many participants reported substantial financial and social barriers to compliance and resistance to compliance in their communities fuelled by COVID-19 denialism, rumours, misinformation, and poorly enforced government restrictions. Conclusion High levels of knowledge and a high perceived susceptibility and severity of disease were not enough to motivate high levels of compliance with the protective measures in the study communities. Financial and social obligations to protective measures, coupled with COVID-19 denialism and rumours, were significant barriers to compliance. Early risk communication interventions should promote contextually appropriate, high-impact, low-cost interventions and tackle emerging rumours and misinformation.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".