Assessment of Knowledge, Attitude, and Practice Towards the Prevention of Monkeypox Among People of Bangladesh With at Least Primary Education: A Cross‐Sectional Study
Bibliographic record
Abstract
ABSTRACT Background and Aims The recent emergence and rapid spread of monkeypox (Mpox) have become a global public health concern. Gaps in proper information dissemination can cause confusion and knowledge divergence among the general public, leading to difficulties in disease management. Therefore, this study aimed to assess the overall knowledge, attitude, and practice (KAP) regarding Mpox among the general population of Bangladesh to help implement optimal public health interventions. Methods A cross‐sectional study was conducted using an online questionnaire survey to evaluate the KAP related to Mpox among the general population of Bangladesh. A total of 1025 KAP domains were registered during the survey, among which 938 were eligible for final analysis. Comparisons between different groups were performed using the Wilcoxon signed‐rank and the Kruskal–Wallis test. Additionally, a multivariable logistic regression analysis was conducted to identify factors associated with williness to practice preventive behaviours against Mpox. Results Only 7.25% (68/938) of participants demonstrated good knowledge, 20.58% (193/938) had positive attitudes, and 25.05% (235/938) were willing to practice preventive measures toward Mpox prevention, indicating overall inadequate KAP levels. The Kruskal–Wallis test revealed statistically significant differences in knowledge scores based on education level ( p value = 0.004), age group ( p value = 0.008), and division ( p value = 0.009). Similarly, significant variations in attitude scores were observed across areas of residence ( p value = 0.003), education level ( p value < 0.001), and age group ( p value = 0.002). Furthermore, both univariate and multivariable logistic regression analyses identified gender, knowledge level, and attitude score as significant predictors of willingness to engage in preventive practices. Conclusion The low KAP scores suggest an urgent need for mass awareness campaigns for Mpox prevention in Bangladesh. The findings of this study could assist policymakers in taking necessary steps to mitigate the potential public health burden associated with Mpox in the country.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".