Knowledge and Response of Rural Women in Nigeria to COVID-19: A Cross-Sectional Study
Bibliographic record
Abstract
Abstract BackgroundNigeria has experienced COVID-19 pandemic as in nearly 200 countries around the world. The objective of this study was to investigate the knowledge, risk perceptions, and preventive practices among rural women in Edo State, Nigeria to identify the social circumstances under which women respond to COVID-19 in the community. MethodThe study design was cross-sectional and consisted of face-to-face interviews with 1,411 women in 20 rural communities in Edo state, south-south Nigeria using a structured questionnaire. Questions consisted of socio-demographic characteristics, the knowledge of COVID-19, its symptoms and prevention method, and the women’s compliance with the prevention guidelines recommended by the Nigerian Centre for Disease Control. Some of the questions and formats were adapted from the survey tool and guidance for monitoring knowledge, risk perceptions and preventive behavior by the WHO Regional Office for Europe. The data were analyzed with univariate, bivariate and multivariable statistical techniques.ResultsThe results showed relatively high knowledge of COVID-19 but low perceptions of risk, and inadequate self-reported compliance with the recommendations for prevention, use of face mask, and handwashing. The most prominent determinants of lack of knowledge of COVID-19, the symptoms, and preventive measures; perception of COVID-19 as no health threat; and poor preventive behavior include less exposure to the media, being in a polygynous and consensual marriage, illiterate, age, and not having a mobile phone.ConclusionsWe conclude that although rural women in Nigeria have relatively high knowledge, low-risk perception and adoption of preventive measures for COVID-19 are grossly inadequate. These deficits are attributable to illiteracy, poor access to information, and the pervading poverty in rural communities. Appropriate policies and programs that address these challenges will prevent COVID-19 pandemic and its consequences in rural Nigeria.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".