Key Considerations for RCCE in the 2022 Ebola Outbreak Response in Greater Kampala, Uganda
Bibliographic record
Abstract
On 20 September 2022, an outbreak of the Sudan strain of Ebola Virus Disease – SVD – was announced as the first laboratory-confirmed patient was identified in a village in Mubende District in central Uganda. Uganda’s Ministry of Health (MoH) activated the National Task Force and developed and deployed a National Response Plan, which includes the activation of District Task Forces. The target areas include the epicentre (Mubende and Kassanda districts) and surrounding areas, as well as Masaka, Jinja and Kampala cities. This is of great concern, as Kampala is the capital city with a high population and linkages to neighbouring districts and international locations (via Entebbe Airport). It is also a serious matter given that there has been no outbreak of Ebola before in the city. This brief details how Risk Communication and Community Engagement (RCCE) activities and approaches can be adapted to reach people living in Greater Kampala to increase adoption of preventive behaviours and practices, early recognition of symptoms, care seeking and case reporting. The intended audiences include the National Task Force and District Task Forces in Kampala, Mukono, and Wakiso Districts, and other city-level RCCE practitioners and responders. The insights in this brief were collected from emergent on-the-ground observations from the current outbreak by embedded researchers, consultations with stakeholders, and a rapid review of relevant published and grey literature. This brief, requested by UNICEF Uganda, draws from the authors’ experience conducting social science research on Ebola preparedness and response in Uganda. It was written by David Kaawa-Mafigiri (Makerere University), Megan Schmidt-Sane (Institute of Development Studies (IDS)), and Tabitha Hrynick (IDS), with contributions from the MoH, UNICEF, the Center for Health, Human Rights and Development (CEHURD), the Uganda Harm Reduction Network (UHRN), Population Council and CLEAR Global/Translators without Borders. It includes some material from a SSHAP brief developed by Anthrologica and the London School of Economics. It was reviewed by the Uganda MoH, University of Waterloo, Anthrologica, IDS and the RCCE Collective Service. This brief is the responsibility of SSHAP.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".