Bolstering community resilience through health-focused climate change adaptation: moving from talk to action in Western Canadian communities
Bibliographic record
Abstract
The impacts of climate change have been recognized as a global health emergency. Worsening climate stressors are resulting in injury, illness and death. As this threat to health grows, so does the need to adapt. Climate change adaptation has been noted to reduce the risk of disease transmission, chronic illness exacerbation, physical trauma, and the mental health impacts associated with climate change. Cities across Western Canada have initiated the process of implementing health-focused climate change adaptation; however, progress has been slow, leaving communities vulnerable to health threats. Exploring five case study communities in Western Canada, this research sheds light on factors that enable and constrain progress on health-focused climate change adaptation. Research objectives are addressed through analysis of 16 key actor interviews (with experts in public health, planning, local governance, and other related fields), and a scan of relevant strategic planning documents. Results indicate that political will, expertise, and awareness has resulted in the development of health-focused climate change adaptation policy/ plans. However, implementation of these plans lags in practice. This lag stems from a lack of implementation detail in climate change adaptation plans, limited understanding of the impact of climate change on health, and jurisdictional confusion.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.059 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.012 |
| 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".