1663. Community Engagement for Ebola Prevention in Eastern Democratic Republic of Congo
Bibliographic record
Abstract
Abstract Background The second largest outbreak of Ebolavirus in history is currently ongoing in Eastern DRC. The epidemic is characterized by social resistance to foreign-led response teams. Trusted local health practitioners, including medical students, may be valuable social mobilizers in this challenging context. Methods We report on a student-led educational campaign to increase community awareness and engagement in EVD control efforts. We evaluated student and community participant satisfaction using standardized questionnaires. Results The outreach was conducted in November 2018, involving parades, speeches, branded banners and T-shirts, and interpersonal interactions in public spaces. Key messages, linked to previously identified resistant attitudes, included: “Ebola exists in Butembo,” “Bring infected family members to the Ebola Treatment Unit,” and “Leave burials to the official team.” Medical students (n = 355) and community participants (n = 319) evaluated the outreach campaign. Satisfaction was high: 320 (90%) students agreed that medical students could contribute to the EVD response effort, and 233 (73%) community members agreed that the students had helped them understand Ebola in the area. Lower satisfaction scores were associated with self-reported “resistant” attitudes (e.g., intention to hide infected family member from authorities, ρ = -0.25, P < 0.0001), denial of the existence of Ebola in the area (ρ = -0.17, P = 0.0018), and mistrust of the foreign response team (e.g., belief in mercenary motive, ρ = -0.11, P = 0.042). Higher satisfaction scores were associated with the view that local engagement was critical to ending the epidemic (ρ = +0.13, P = 0.017). Both students (77%) and community members (71%) agreed that they were more motivated to combat Ebola as a result of the outreach, suggesting that the activities fostered empowerment. Conclusion Medical students can lead satisfactory community engagement and educational activities during an EVD epidemic. As trusted local health agents, medical students may be valuable allies in building public trust and cooperation in this epidemic complicated by social resistance. 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".