Adaptation to climate change in the Ontario public health sector
Bibliographic record
Abstract
BACKGROUND: Climate change is among the major challenges for health this century, and adaptation to manage adverse health outcomes will be unavoidable. The risks in Ontario - Canada's most populous province - include increasing temperatures, more frequent and intense extreme weather events, and alterations to precipitation regimes. Socio-economic-demographic patterns could magnify the implications climate change has for Ontario, including the presence of rapidly growing vulnerable populations, exacerbation of warming trends by heat-islands in large urban areas, and connectedness to global transportation networks. This study examines climate change adaptation in the public health sector in Ontario using information from interviews with government officials. METHODS: Fifty-three semi-structured interviews were conducted, four with provincial and federal health officials and 49 with actors in public health and health relevant sectors at the municipal level. We identify adaptation efforts, barriers and opportunities for current and future intervention. RESULTS: Results indicate recognition that climate change will affect the health of Ontarians. Health officials are concerned about how a changing climate could exacerbate existing health issues or create new health burdens, specifically extreme heat (71%), severe weather (68%) and poor air-quality (57%). Adaptation is currently taking the form of mainstreaming climate change into existing public health programs. While adaptive progress has relied on local leadership, federal support, political will, and inter-agency efforts, a lack of resources constrains the sustainability of long-term adaptation programs and the acquisition of data necessary to support effective policies. CONCLUSIONS: This study provides a snapshot of climate change adaptation and needs in the public health sector in Ontario. Public health departments will need to capitalize on opportunities to integrate climate change into policies and programs, while higher levels of government must improve efforts to support local adaptation and provide the capacity through which local adaptation can succeed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".