1666. Community Beliefs about Ebola and Implications for Disease Control in Eastern Democratic Republic of the Congo
Bibliographic record
Abstract
Abstract Background The current Ebola epidemic in Eastern Democratic Republic of the Congo (DRC) has surpassed 1,300 cases and 800 deaths. Social resistance is a major barrier to control efforts, and invites an exploration of community beliefs around Ebola and its origins. Methods Mixed-methods study, using focus group discussions (FGDs) with key community informants and a 19-item survey questionnaire broadly sampling the outbreak zone. Results Between 4 to 17 August, 2018, we conducted 4 FGDs (20 participants) and surveyed 286 community members across Eastern DRC. FGDs revealed a widespread rumor in Mangina early in the epidemic of two twins bewitched by their aunt after eating her cat, who developed bleeding symptoms and triggered the epidemic. However, this myth appeared to dissipate as the epidemic progressed and biomedical transmission became generally accepted (medical syncretism). In our survey, 6% of respondents endorsed supernatural origins of Ebola. This subgroup did not differ from other respondents in terms of knowledge of biomedical modes of transmission or resistant attitudes toward infection control measures, but was more likely to believe that traditional healers could cure Ebola. Wild animals of the forest were recognized as sources of the Ebola virus by 53% of survey respondents. Our findings suggest that skepticism and/or denial of the biomedical discourse, coupled with and mistrust and fear of ETUs may fuel “underground” transmission of Ebola outside western-style medical facilities, as patients seek care from traditional healers, who are ill-equipped to deal with a highly contagious biohazard. Conclusion A deeper understanding of beliefs around Ebola origins may illuminate strategies to engage communities in control efforts. Disclosures All authors: No reported disclosures.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".