Determinants of COVID-19 knowledge and self-action among African women: Evidence from Burkina Faso, the Democratic Republic of Congo, Kenya, and Nigeria
Bibliographic record
Abstract
Knowledge of infectious diseases and self-action are vital to disease control and prevention. Yet, little is known about the factors associated with knowledge of and self-action to prevent the coronavirus disease (COVID-19). This study accomplishes two objectives. Firstly, we examine the determinants of COVID-19 knowledge and preventive knowledge among women in four sub-Saharan African countries (Kenya, Nigeria, the Democratic Republic of Congo, and Burkina Faso). Secondly, we explore the factors associated with self-action to prevent COVID-19 infections among these women. Data for the study are from the Performance for Monitoring Action COVID-19 Survey, conducted in June and July 2020 among women aged 15-49. Data were analysed using linear regression technique. The study found high COVID-19 knowledge, preventive knowledge, and self-action among women in these four countries. Additionally, we found that age, marital status, education, location, level of COVID-19 information, knowledge of COVID-19 call centre, receipt of COVID-19 information from authorities, trust in authorities, and trust in social media influence COVID-19 knowledge, preventive knowledge, and self-action. We discuss the policy implications of our findings.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".