‘They came with cholera when they were tired of killing us with bullets’: Community perceptions of the 2010 origin of Haiti’s cholera epidemic
Bibliographic record
Abstract
In 2010 following a catastrophic earthquake, Haiti saw the beginning of what would become the world’s largest cholera epidemic.1 Soon after the beginning of the outbreak, and later proven by genomic and epidemiological analysis,2 Nepalese United Nations (UN) peacekeepers were implicated as the source of cholera.3,4 Our research examines Haitian community beliefs and perceptions, six-and-a-half years after the outbreak began, regarding the origin of Haiti’s cholera outbreak. A narrative capture tool was used to record micronarratives of Haitian participants surrounding ten UN bases across Haiti. Seventy-seven micronarratives focusing on cholera were selected for qualitative analysis from a larger dataset. Three themes emerged: who introduced cholera to Haiti, how cholera was introduced to Haiti, and preventative measures against cholera. With varying levels of confidence, the origins of the epidemic were recognized as a result of the actions of the UN and associated Nepalese peacekeepers, exhibiting a distrust of foreign intervention in Haiti. Participants also expressed frustration with Haiti’s inadequate water and sanitation infrastructure that facilitated the widespread transmission of cholera and the cholera-induced preventative measures thrust upon them. This study reinforces the need for additional transparent communication from the UN to address ongoing misconceptions surrounding the cholera outbreak. Humanitarian assistance to the Global South must be considered within the context of the existing infrastructure and of the country receiving aid, especially on the heels of upheaval caused by a disaster such as the 2010 earthquake. Furthermore, action is needed to improve water and sanitation practices in Haiti to prevent further spread of waterborne illness.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".