From theory to practice: a Canadian case study of the utility of climate change adaptation frameworks to address health impacts
Bibliographic record
Abstract
OBJECTIVE: It is now recognized that climate change affects human health. The question is how to adapt. This article examines mainstreaming climate considerations into public health programs and the utility of climate change and health adaptation frameworks, using Ontario, Canada, as a case study. METHODS: A literature review identified climate change and health adaptation frameworks for comparison with the Ontario Public Health Standards. Key informant interviews gauged the extent to which climate change risks are currently considered in policy and planning. RESULTS: Ontario's Public Health Standards already require many of the risk management activities identified in climate change and health adaptation frameworks. However, public health officials require additional information about linkages between climate change and health to manage risks. CONCLUSIONS: Risk management activities such as population health assessments, surveillance and public education and outreach can address many key risks related to climate hazards when information about the risks, vulnerable populations and time scales is made available to health officials. The development, analysis and transfer of this information should be considered a priority at all levels within the public health sector.
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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 | 0.022 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.043 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".