Health effects of flooding in Canada: A 2015 review and description of gaps in research
Bibliographic record
Abstract
Worldwide, floods are the most common natural disaster and produce a broad array of health impacts. In Canada, it is difficult to quantify all of the health impacts associated with flooding. This data gap is salient because of the increasing risks floods are posing to society as a result of climate change. This paper reviews the epidemiological evidence of flood-related health effects, and the Canadian susceptibility to these effects. The health impacts from flooding range from mortality, drowning and other injuries to hypothermia, mental health impacts, deterioration of elderly/patients who required emergency transportation, homelessness and transmission of contagious diseases and others. The Canadian population includes a range of socio-economic levels, demographics and pre-existing illness. Additionally, many Canadians live and work in flood plains or on shorelines where floods are a hazard. There is, therefore, variable susceptibility to flood events in the population. Susceptibility to these health impacts can be buffered by the population’s adaptive capacity and external support. To ensure the safety and health of the Canadian population, government and non-profit organizations can decrease the burden of flooding by increasing adaptive capacity, emergency response and a focus on preventative measures. Vulnerable populations who require extra support to avoid negative health impacts from flooding, such as First Nations populations, need further attention. Recommendations are offered for addressing the health-related effects of flooding.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.031 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".