Coronavirus Disease 2019 (COVID-19): Knowledge, attitudes, practices (KAP) and misconceptions in the general population of Katsina State, Nigeria
Bibliographic record
Abstract
Abstract Introduction Over six million cases of Coronavirus Disease 2019 (COVID-19) were reported globally by the second quarter of 2020. The various forms of interventions and measures adopted to control the disease affected people’s social and behavioural practices. Aim This study aims to investigate COVID-19 related knowledge, attitudes and practices (KAP) as well as misconceptions in Katsina state, one of the largest epicentres of the COVID-19 outbreak in Nigeria. Methods The study is a cross-sectional survey of 722 respondents using an electronic questionnaire through the WhatsApp media platform. Results One thousand five hundred (1500) questionnaires were sent to the general public with a response rate of 48% (i.e. 722 questionnaires completed and returned). Among the respondents, 60% were men, 45% were 25-39 years of age, 56% held bachelor’s degree/equivalent and above and 54% were employed. The study respondents’ correct rate in the knowledge questionnaire was 80% suggesting high knowledge of the disease. A significant correlation ( P < 0.05) exists between the average knowledge score of the respondents and their level of education (τ b = 0.16). Overall, most of the respondents agreed that the COVID-19 will be successfully controlled (84%) and the Nigerian government would win the fight against the pandemic (71%). Men were more likely than female ( P < 0.05) to have recently attended a crowded place. Being more educated (bachelor’s degree or equivalent and above vs diploma or equivalent and below) is associated with good COVID-19 related practices. Among the respondents, 83% held at least one misconception related to COVID-19, with the most frequent being that the virus was created in a laboratory (36%). Respondents with a lower level of education received and trust COVID-19 related information from local radio and television stations and respondents at all levels of education selected that they would trust health unit and health care workers for relevant COVID-19 information. Conclusion Although there is high COVID-19 related knowledge among the sample, misconceptions are widespread among the respondents. These misconceptions have consequences on the short- and long-term control efforts against the disease and hence should be incorporated in targeted campaigns. Health care related personnel should be at the forefront of the campaign.
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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 | 0.002 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".