Knowledge, risk perception and adherence to COVID-19 prevention advisory among police officers in Makurdi Metropolis Benue State, 2020.
Bibliographic record
Abstract
INTRODUCTION: April 2020, 668 confirmed cases, 22 deaths and 188 recoveries have been reported. Police officers are at the forefront of enforcing advisories to ensure public compliance. However, there is a paucity of data on knowledge, risk perception, and adherence to COVID-19 advisories issued by the Health authorities particularly among the police officers. We, therefore, assessed the knowledge, risk perceptions and adherence to NCDC recommended advisory on COVID-19. METHODS: we conducted a two-stage sampling cross-sectional study among different cadres of police officers in Benue State, Nigeria using a pretested, semi-structured, interviewer-administered questionnaire. The results of the study were presented in frequencies and proportions. Chi-square test was used for an association between variables at p-value < 0.05. RESULTS: = 112.5, p = 0.001) were found to be associated with good adherence. CONCLUSION: while most participants had a good knowledge of COVID-19 transmission dynamics, and positive risk perception about COVID-19, good adherence to public health advisories were low. We recommended periodic training, provision of adequate PPE and personal hand-sanitizers as a strategy to improve adherence.
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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 | 0.000 | 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 | 0.001 | 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".