The Climate Change, Food Security and Human Health Nexus in Canada: A Framework to Protect Population Health
Bibliographic record
Abstract
Climate change impacts on the Canadian food system pose risks to human health. Little attention has been paid to the climate change, food security, and human health nexus, resulting in a number of knowledge gaps regarding food system components that are most vulnerable to climate change. The lack of understanding of key dynamics and possible future impacts challenges the ability of public health officials and partners in other sectors to prepare Canadians for future health risks. A series of literature reviews were conducted to establish the relationship between climate change, food security, and human health, and to identify vulnerabilities within the Canadian food system. Evidence suggests that key activities within the food system are vulnerable to climate change. The pathways in which climate change impacts travel through the food system and affect the critical dimensions of food security to influence human health outcomes are complex. Climate-related disruptions in the food system can indirectly impact human health by diminishing food security, which is a key determinant of health. Human health may also be directly affected by the physical effects of climate change on the food system, primarily related to the impacts on nutrition and foodborne illnesses. In this study, we propose a novel analytical framework to study and respond to the climate change, food security, and human health nexus. This work is intended to help public health officials, researchers, and relevant stakeholders investigate and understand current and future risks, and inform adaptation efforts to protect the health of Canadians.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".