Knowledge, attitudes, and practices toward COVID-19 among the general population: a cross-sectional study in Kankan, Guinea
Bibliographic record
Abstract
Background: Implementing decisive and effective infection prevention and control measures necessitates a thorough grasp of the general population's level of knowledge in order to identify existing gaps and react appropriately. Objective: The goal of this cross-sectional research was to assess public knowledge, attitudes, and practices (KAP) about COVID-19 in Kankan Guinea, in order to better understand the socio-demographic factors that are associated with poor KAP. Materials and Methods: The study population consists of 1230 people who reside in five health districts in the Kankan region. An anonymous paper-based questionnaire, given face-toface by trained field agents, was used to gather data. Results: The research included 1230 Guineans in total. The bulk of respondents (60%) were familiar with COVID-19. Only 44% of respondents under the age of 29 had a clear understanding of COVID-19. Male participants knew more about COVID-19 than female ones (P=0.003). The majority of participants (82%) had negative attitudes toward COVID-19, while 61% reported positive practices linked to COVID-19 measures. In this research, being female was a risk factor for poor knowledge of COVID-19 (P0,001), and being single was a risk factor for negative attitudes toward COVID-19 (P=0,009). Conclusion: Appropriate measures should be taken to increase public awareness and improve general practice of preventive measures aimed at reducing the spread of infectious diseases such as 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".