COVID-19 in Sub-Saharan African Countries: Association between Compliance and Public Opinion
Bibliographic record
Abstract
BACKGROUND: The outbreak of coronavirus disease (COVID-19) has created a global public health crisis and non-compliance with public health measures to contain the infection poses a challenge to Sub-Saharan African governments. This study investigated the associations between compliance and public opinion on COVID-19 public health containment measures across selected SSA countries. METHOD: Anonymous online cross-sectional survey was administered to 1779 adults (18 years and older) during the mandatory lockdown period in most African countries (April 18 - May 16, 2020). Respondents were recruited via Facebook, WhatsApp, and authors' networks. Data on participants’ socio-demographics, their opinions regarding the precautionary measures against COVID-19, and their compliance with preventive measures were collected. Multiple logistic regression analysis was used to examine the association between compliance and public opinions about COVID-19. RESULTS: Respondents who did not think that public health authorities in their countries were doing enough to control the C0VID-19 outbreak were more likely to attend crowded places (aOR 1.75, 95% CI 1.30-2.35). Those who thought COVID-19 would not remain in their countries (aOR 0.48, 95% CI 0.24 - 0.96) and those who thought self-isolation is not needed during the pandemic (aOR 0.29, 95% CI 0.13 - 0.65) were less likely to encourage others to comply with the strategies put in place to prevent the spread of the disease. Participants who thought the COVID-19 outbreak was dangerous and those wearing medical masks were found to wash their hands with soap under running water. CONCLUSION: The study showed that public opinion influenced the compliance of individuals to public health measures for containment and mitigation of COVID-19. There is a need to improve compliance by the public.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".