Knowledge, attitude, and practice assessment toward COVID-19 among communities in East Nusa Tenggara, Indonesia: A cross-sectional study
Bibliographic record
Abstract
Objective This research aimed to assess the knowledge, attitude and practice toward COVID-19 among East Nusa Tenggara (NTT) population and identify associated sociodemographic factors. Methods A cross-sectional study was performed among communities in 22 regencies of NTT between October–November 2021 using a structured and validated questionnaire that consists of 22-items of knowledge, 6-items of attitude and 13-items of practice related COVID-19 questions. A cut off point of at least 70% was employed to categorize good knowledge, attitude, and practice. Results The percentages of survey respondents showing good knowledge, attitude and practice toward COVID-19 were 79.8, 72.7, and 94.6%, respectively. There was a significant positive, though weak, linear correlation between knowledge and practice scores (ρ = 0.097; p = 0.049). Knowledge was strongly associated with regency of residence (Cramer's V = 0.266; p = 0.010), education (Cramer's V = 0.312; p < 0.001), and occupation (Cramer's V = 0.313; p < 0.001). Attitude and practice had strong relationship with regency of residence (Cramer's V = 0.289; p = 0.024) and education (Cramer's V = 0.272; p < 0.001), respectively. Conclusion Nearly all survey participants showed good precautionary behaviors, but there was still a quarter of respondents indicated poor knowledge and attitude. There was also a positive relationship between knowledge and practice scores thereby indicating the importance of local public health advocates to distribute information uniformly especially to the groups with inadequate knowledge toward COVID-19 as a means to control the virus transmission.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".