Corona virus-19 preventive practices among primary health workers in Owo local government, Ondo state Nigeria
Bibliographic record
Abstract
Background: Coronavirus disease 2019 (COVID-19) is an infectious disease with high mortality. Healthcare workers are at the frontline of COVID-19 response and are prone to infection. Therefore, healthcare workers’ preventive practices cannot be underestimated. The study aimed to determine the COVID-19 preventive practices among primary health workers in Owo, Local Government, Ondo state Nigeria.Methods: This was a descriptive cross-sectional study. Consenting staff of primary health centres completed a pretested self-administered questionnaire. The data were analysed using descriptive and inferential statistics.Results: A total of 400 respondents were recruited with 91 (22.8%) males and 309 (77.2%) females giving male to female ratio of 1:3.4. The age range of the respondents was 19-61 years with a mean age of 37.1 (8.1) years. More than half (58.0%) had tertiary level of education and most participant were community health extension workers (36.7%). Majority (99.8%) of the workers were aware of COVID-19 though 212 (53.0%) had good knowledge. The major source of information was the television (94.3%). About 351 (87.8%) had positive attitude despite 383 (95.7%) agreeing that COVID-19 is a problem in Nigeria. More than three-quarter (76.5%) had good practice. There was a significant relationship between knowledge (χ2=29.072, p<0.001), attitude (χ2=35.156, p<0.001) with practice. Educational level was the only factor associated with adherence to COVID-19 guideline (χ2=5.256; p=0.022). The predictors of good practice include knowledge (95% CI =2.296-6.269; p<0.001) and attitude (95% CI =3.079-10.767; p<0.001).Conclusions: The health workers had good knowledge, positive attitude and good preventive practices towards COVID-19.
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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 | 0.000 | 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) | 0.002 | 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".