Climate Change and Vibrio cholerae in Herring Eggs: The Role of Indigenous Communities in Public Health Outbreak Responses
Bibliographic record
Abstract
Climate change brings about novel types of public health emergencies. Unforeseen challenges put additional pressure on health systems and require innovative approaches to address emerging needs. The health of Indigenous Peoples is particularly impacted by the changing climate, because of their close connection to the land. For instance, the physical, emotional, mental, and spiritual well-being of coastal First Nations in British Columbia (BC), Canada, is interconnected with the abundance of healthy marine food sources that form the base of local traditional diets. The 2018 discovery of Vibrio cholerae illness in those who had eaten contaminated herring eggs not only had a clinical health impact but also created concerns for the safety of local food systems. The limited magnitude of the outbreak demonstrates the critical importance of collaborative partnerships between coastal First Nations communities in BC and health authorities working together in outbreak investigations. Yet, the lack of procedures that address cultural and institutional differences led to unnecessary discrepancies in the approach. This paper introduces the public health intervention used during the first ever Vibrio cholerae outbreak in coastal BC. The intervention has the potential to inform best practices when developing emergency response protocols potentially affecting Indigenous people and traditional foods. In this qualitative case study of the formal institutional documents and narratives of the key partners involved in the response, we assess the intervention, highlight the challenges and enablers, share lessons learned, and identify knowledge requirements to improve confidence in the traditional food system and support early warning systems.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".