Summary: Canada’s Food-borne Illness Outbreak Response Protocol
Bibliographic record
Abstract
BACKGROUND: The burden of illness due to food-borne pathogens each year in Canada is significant. Investigations of food-borne illness outbreaks, particularly those with cases in more than one jurisdiction, are complex. Accordingly, efficient outbreak response requires the coordination and collaboration of many investigative partners. OBJECTIVE: (FIORP), the primary guidance document for investigations of multi-jurisdictional food-borne illness outbreaks in Canada. APPROACH: The current version of the FIORP was developed in 2010 by the Public Health Agency of Canada following consultation with Health Canada, the Canadian Food Inspection Agency, and provincial and territorial stakeholders. RESULTS: The FIORP outlines guiding principles and operating procedures to enhance collaboration and coordination among multiple investigative partners in response to multi-jurisdictional food-borne illness outbreaks. It has provided guidance for the conduct of 22 such investigations led by the Public Health Agency of Canada's Centre for Food-borne, Environmental and Zoonotic Infectious Diseases between 2011 and 2013. Furthermore, it has also served as a guide for the development of provincial protocols. CONCLUSION: The timely and effective investigation of and response to multi-jurisdictional food-borne illness outbreaks in Canada is facilitated and enhanced by the FIORP.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".