Knowledge, Attitude, and Practice of Preventive Measures Against COVID-19 among Pregnant Women Receiving Antenatal Care in Calabar, Nigeria
Bibliographic record
Abstract
Background: Implementation of preventive precautions remains the most important measure in the control of coronavirus 2019 (COVID-19) infection. This study was aimed at evaluating the extent of knowledge, attitude, and practice of COVID-19 prevention among pregnant women in Calabar, Nigeria. Methodology: Cross-sectional descriptive design and systematic random sampling method were utilized to recruit antenatal care clinic attendees, in the University of Calabar Teaching Hospital, Calabar, Nigeria. Study variables were assessed using structured questionnaires. Information was entered and analyzed with SPSS version 21.0. A percentage knowledge score of at least 75% was considered satisfactory. P-value was set at 0.05. Result: Two hundred and eighty-four women were studied and the mean age was 30.6 ± 5.0 years. Approximately half of the women (51.4%) were within the third trimester of pregnancy. The mean percentage knowledge score was 71.7% ±17.2%, and the overall level of knowledge was unsatisfactory in 43.3% of respondents. Most women agreed with the reality of existence of COVID-19 infection (90.1%), and 30.6% were of the opinion that the pandemic could be eradicated by prayers alone. Most women practised preventive measures including the use of face mask (89.1%), social distancing (84.2%), and regular handwashing (94.4%). There was a significantly higher mean total knowledge score as well as knowledge of preventive measures among users compared with non-users of face mask, and regular subjects were compared with non-regular subjects with regards to their handwashing practice ( P < 0.05). Conclusion: Familiarity with COVID-19 prevention among pregnant women in the study context is suboptimal. There is a need to improve maternal health education provided during antenatal care visits, toward addressing misconceptions related to the pandemic.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".