Health in the climate crossroads : exploring the integration of health in climate policy and practice in Canada
Bibliographic record
Abstract
This doctoral thesis critically investigates the integration of health into climate adaptation policy and practice within the Canadian context. Through four empirical chapters, three of which have a particular focus on municipalities in British Columbia, it explores how health is understood, planned for, and acted upon in relation to climate change. This work is grounded in an analysis of interviews, planning documents, survey data, and a regional case study. Chapter 2 presents qualitative findings from interviews with local government leaders, examining their perceptions of climate-related health risks and their experiences with adaptation. It highlights governance gaps and calls for more multi-sectoral collaboration. Chapter 3 analyzes municipal climate action plans across British Columbia to assess how health is incorporated. While health risks are often acknowledged, the study finds limited integration of health systems or consideration of health co-benefits. The chapter recommends advancing a “Health in All Climate Policies” approach to strengthen cross-sector collaboration and public health responsiveness. In Chapter 4, survey data reveal that 23% of Canadians report experiencing physical health impacts from extreme weather events. Additionally, the survey shows that attitudes towards adaptation are influenced by a complex interplay of risk perception, health awareness, community belonging and demographic characteristics. The final chapter, Chapter 5, examines the implementation of cooling and cleaner air shelters in Metro Vancouver. Drawing on interviews with public and non-profit professionals, it identifies key lessons on service design, access, and the importance of sustained collaboration across sectors to protect vulnerable populations during extreme heat and wildfire smoke events. Overall, this thesis argues for stronger integration of health into climate policies and greater collaboration between health systems, local governments, and communities to protect public health in a rapidly changing climate.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".