A community-engaged infection prevention and control approach to Ebola
Bibliographic record
Abstract
The real missing link in Ebola control efforts to date may lie in the failure to apply core principles of health promotion: the early, active and sustained engagement of affected communities, their trusted leaders, networks and lay knowledge, to help inform what local control teams do, and how they may better do it, in partnership with communities. The predominant focus on viral transmission has inadvertently stigmatized and created fear-driven responses among affected individuals, families and communities. While rigorous adherence to standard infection prevention and control (IPC) precautions and safety standards for Ebola is critical, we may be more successful if we validate and combine local community knowledge and experiences with that of IPC medical teams. In an environment of trust, community partners can help us learn of modest adjustments that would not compromise safety but could improve community understanding of, and responses to, disease control protocol, so that it better reflects their 'community protocol' (local customs, beliefs, knowledge and practices) and concerns. Drawing on the experience of local experts in several African nations and of community-engaged health promotion leaders in the USA, Canada and WHO, we present an eight step model, from entering communities with cultural humility, though reciprocal learning and trust, multi-method communication, development of the joint protocol, to assessing progress and outcomes and building for sustainability. Using examples of changes that are culturally relevant yet maintain safety, we illustrate how often minor adjustments can help prevent and treat the most serious emerging infectious disease since HIV/AIDS.
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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 | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".