Perceived Risk and Associated Factors towards COVID-19 infection among the residents of Ondo State, Southwest Nigeria
Bibliographic record
Abstract
BackgroundPerceived risk is an important factor in understanding why and how a population adopts health-prevention interventions. When diseases are perceived as low-risk, motivation to use and adhere to prevention interventions is low, which can aid the spread of highly infectious diseases such as COVID-19. In this study, we assessed risk perception and its associated factors towards COVID-19 infection among the residents of Ondo State, southwest Nigeria. MethodsA community-based, cross-sectional study design using a multi-stage sampling technique was used to recruit 593 heads of households in three local government areas (LGA) in Ondo State. Data were collected using an interviewer-administered questionnaire which captured information on sociodemographic variables, knowledge of COVID-19 prevention, COVID-19 infection prevention and control practices, and risk perception from September 1 to 30, 2021. ResultsThe respondents were mostly males 357 (60.2%). The mean age of respondents was 37.5±14.7 years with 409 (69%) between the age group of 20 to 40 years. Slightly more than one quarter of respondents were civil servants and 78.4% were of Yoruba ethnicity. More than three quarters, 522 (88%), of the respondents had good knowledge of COVID-19 and its preventive measures. The mean risk perception score was 49.0±12.1. Respondents who were civil servants, had good knowledge of COVID-19 and its preventive measures, had lower household income, and were of Yoruba ethnicity had significantly higher risk perception towards COVID-19 infection compared to other groups. Higher risk perception was associated with preventive measures, such as handwashing. ConclusionOur study shows a high-risk perception towards COVID-19 infection among residents in Ondo state, Nigeria. However, there were significant differences between varying knowledge levels, ethnic groups and civil versus non-civil servants. In view of this, we recommend intensified risk communication interventions targeting these groups to improve their risk perception to change health-protective behaviour towards COVID-19 infection.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".