Disaster Research Response Development in Canada
Bibliographic record
Abstract
The 2016 Alberta wildfires, which destroyed entire neighbourhoods of urban Fort McMurray and the 2013 Lac-Mégantic, Québec train derailment and explosion illustrate that Canadian communities are not immune to environmental public health (EPH) disasters. Disasters expose the population, responders and volunteers to a range of contaminants and stressors, which may harm physical and mental health. When disasters strike, the initial focus is on life saving interventions such as clinical care and measures aiming at minimizing population exposure including evacuation, sheltering in place and do-not-consume advisories. Afterward, attention shifts toward community re-entry, rehabilitation and health studies to address potential delayed and long-term health effects.Exposure science and environmental epidemiology resources can play a vital role in supporting response authorities to reduce the health risks from the release of hazardous chemicals. They may contribute to the timely identification, determination of concentrations and dispersion of released substances, designing questionnaires and initiating registries, and evaluating the value of biological sampling. Further, EPH disasters typically offer a brief window of time to collect ephemeral exposure data, biospecimens and to start scientific research that could improve both health outcomes and capabilities for future response.This discussion will highlight the efforts of the Canadian Disaster Research Response (CanDR2) Steering Committee to develop a Pan-Canadian framework aiming at enhancing the integration of EPH scientific and research assets into disaster management. Three EPH focus areas will be presented: (1) Establishing a disaster response community of practice or network (2) Generating and transferring disaster knowledge, and (3) Enhancing timely data sharing, sample collection and health research execution.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".