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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".