Knowledge, Adherence Mapping of Covid-19 Preventive Measures Among Adults in Southwest Nigeria: A Cross-Sectional Study
Bibliographic record
Abstract
Background: COVID-19 preventive measures represent the largest public health interventions in human history. However, gap still exist in the knowledge and adherence towards the preventive measures of the disease. This study assessed the knowledge and adherence towards COVID-19 preventive measures among adults in selected communities of Ekiti state, Southwest Nigeria Methodology: This is a cross-sectional analytical study with multi-stage sampling technique. A pre-tested, structured questionnaire was used to collect data. Statistical Package for the Social Sciences version 23 and ArcGIS version 10.5 were used for data analysis and adherence mapping respectively. Level of statistical significance was set at p<0.05. Result: Majority, 593 (98.8%), knew the preventive measures of COVID-19 and more than three quarter demonstrated good knowledge, 460 (76.7%). Less than half, 269 (44.8%), had good adherence to the preventive measures of COVID-19. Age and higher educational status were found to be associated with knowledge while socio-economic status was associated with adherence (p<0.05). Marriage was found as a predictor of knowledge [(aOR=7.43 (0.85-65.01); p=0.038)] and female respondents were about twice likely than the males to have good adherence to preventive measures. [(aOR=1.78 (1.17-2.69); p=0.007)]. The rich respondents were three times more likely to adhere to preventive measures of COVID-19 [(aOR=3.15 (1.51-6.55); p=0.002)]. Conclusion: The knowledge of COVID-19 preventive measures was high, but adherence to it was poor. The government and other relevant stakeholders in the State need to institute various interventions like health awareness campaign to increase adherence to the preventive measures.
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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 | 0.004 | 0.005 |
| 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 | 0.001 |
| 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 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".