Assessing mpox knowledge and sexual behaviours within high-risk populations in the Democratic Republic of the Congo
Bibliographic record
Abstract
BACKGROUND: Historically, the Democratic Republic of the Congo (DRC) has faced the greatest public health burden from mpox, including more than 70 000 probable cases from 1 January 2024 to 2 February 2025. However, there has been a relative paucity of investigation focused on mpox community engagement in DRC, including assessments of disease knowledge and risk perception. METHODS: Given the ongoing Clade I mpox public health emergency of international concern, and the linkage between sustained human-to-human transmission and dense sexual networks, we sought to investigate mpox knowledge and sexual behaviours among key populations. Between 20 March 2024 and 25 August 2024, we recruited 2794 participants distributed across Kinshasa, Kwango and North Kivu provinces, with a focus in urban centres where mpox risk was considered high. RESULTS: Most participants were considered other at-risk populations (948; 33.9%), followed by men who have sex with men (MSM, 828; 29.6%) and sex workers (897; 32.1%). Mpox knowledge, including transmission routes as well as sexual and health-seeking behaviours, was evaluated through questionnaires led by peer educators. Overall, only 6.1% of all participants reported prior mpox knowledge. Among this participant subset, zoonosis ('direct contact with infected animals') and 'people living in high-risk areas' were the most frequently selected options in regard to mpox transmission and populations at risk, respectively. When considering at-risk behaviours for mpox, those who identified as sex workers reported significantly higher risk sexual activities, including multiple sexual partners (80.3% of sex work participants), engaging in transactional sex (84.7%) and anonymous sex (80.8%) compared with MSM. However, both sex workers (44.8%) and MSM (56.7%) reported the highest health seeking behaviours for a suspected sexually transmitted infection. CONCLUSIONS: Our results highlight the need to evaluate knowledge in high-risk communities, especially in an endemic setting. Community engagement, which incorporates both mpox knowledge and risk perception activities, is inclusive of at-risk populations and is needed for ongoing mpox containment and mitigation efforts.
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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 | 0.003 |
| 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 | 0.001 |
| 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".