Investigating the extent and quality of health-focused climate adaptation planning: Insights from Western Canadian cities
Bibliographic record
Abstract
Climate change is resulting in morbidity and mortality across all regions of the globe. As temperatures continue to rise, the threat to health grows. Regardless of potential mitigation measures, some degree of warming in the near future is unavoidable. As a result, climate adaptation strategies targeted to protecting human health are essential. Local governments have a key role to play in health-focused adaptation, given their strong understanding of local health impacts, knowledge of the local population, and ability to implement local interventions. However, research demonstrates that local governments struggle to make meaningful progress on health-focused adaptation, and are often underprepared to face climate-health risks. Set in the Western Canadian context, this study applies a case study methodology (plan content analysis and key actor interviews) to explore the extent and quality of health-focused climate adaptation planning in five case study cities. Results indicate that although health-focused adaptation planning has been initiated within case study cities, various weaknesses exist within plans. Key weaknesses include a lack of climate-health information, a narrow focus on heat, and missing implementation details. Ultimately, cities note that they are struggling to make progress on their health-focused adaptation agendas, and describe themselves as unprepared to face climate-related health risks. Recommendations to improve health-focused adaptation planning are provided.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.028 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".