Title: Assessing Health Vulnerability and Adaptation to Climate Change: Health Canada's Guidance for Public Health Officials
Bibliographic record
Abstract
Climate variability and change are increasing risks to the health of Canadians in communities across Canada. Addressing the challenges posed by climate change requires knowledge of risks from current climate hazards, projected impacts of future climate change, the unique vulnerabilities facing specific populations, communities or regions, and measures to effectively protect health. An increasing number of health authorities from local to national levels in Canada are undertaking assessments to expand understanding of climate change impacts on health and adaptation options, educate and engage stakeholders and the public and influence policy development. Climate change and health vulnerability and adaptation assessments: (1) Provide information on the expected distribution and severity of future climate change and health impacts to health and emergency management officials, stakeholders and the public; (2) Inform efforts to mainstream information on the health impacts of climate change into existing policies and programs and/or develop new initiatives to reduce the health impacts of climate change; and (3) Support the development of inter-sectoral relationships and collaborations to influence upstream determinants of health in the context of greater climate change stressors.Building on assessment guidance developed by the World Health Organization, Health Canada developed the “Climate Change and Health Vulnerability and Adaptation Assessment Workbook” that integrates greater considerations of gender and Indigenous Population issues and knowledge into assessment steps along with guidance and indicators for gauging the resilience of health systems and services to climate change impacts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.014 |
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".