Coronavirus Disease 2019 (COVID-19): A Cross-Sectional Survey of the Knowledge, Attitudes, Practices (KAP) and Misconceptions in the General Population of Katsina State, Nigeria
Bibliographic record
Abstract
Over six million cases of Coronavirus Disease 2019 (COVID-19) were reported globally by the second quarter of 2020. This study assessed the COVID-19 related knowledge, attitudes, practices and misconceptions in Katsina state, Nigeria. The study is across-sectional survey of 722 respondents using an electronic questionnaire through the WhatsApp media platform. One thousand five hundred questionnaires were sent to the general public with a response rate of 48%. Among the respondents, 60% were men, and 56% held bachelor’s degree and above. The respondents have good knowledge of COVID-19 (80% correct rate on questions related to knowledge). Being more educated is associated with both higher average COVID-19 knowledge score and positive COVID-19 related practices. Overall, >70% of the respondents have a positive attitude towards successful COVID-19 control. Male were more likely than female (Fisher’s exact test P value < 0.05) to have recently attended a crowded place. Among the respondents, 83% held at least one misconception related to COVID-19. Respondents at all levels of education frequently chose to trust health unit and health care workers for relevant COVID-19 information. In conclusion, although there is high COVID-19 related knowledge among the respondents, misconceptions are widespread among them. These misconceptions have consequences on the short- and long-term control efforts against the disease and hence should be incorporated in targeted campaigns. Healthcare related personnel should be at the forefront of the campaign.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".