CLIMATE CHANGE AND PUBLIC HEALTH ADAPTATION PROGRAM: CANADIAN PERSPECTIVE
Bibliographic record
Abstract
Background : The impacts of climate change can place significant demand on health, particularly among vulnerable populations and regions. The need for adaptation strategies to prepare for, and address, the risks and impacts of a changing climate has been widely recognized. Public health professionals play an essential role in knowledge translation and transfer of science-based information and development of integrated education, surveillance and response systems to reduce health risks. This project will address gaps and identify public health adaptation strategies to address the impacts of climate change on vector-borne and water-borne infectious diseases in Canada. Methods:The climate change and adaptation program consists of two components: university research and community/regional based pilot projects. The university research projects are developing tools to assess community vulnerability to water-borne and/or vector-borne diseases. The community/regional based pilot projects have undertaken activities to examine infectious disease risks and adaptation associated with climate change in their regions. . Results:Tools for public health (i.e. risk maps, forecasting models), resulting from the university research, are being produced to identify the predicted risk of water-borne and vector-borne (specifically Lyme disease and West Nile virus) diseases now and in the future with from a changing climate. From the pilot projects a synthesis report of the findings, lessons learned and “best practices” was created to provide information on how health regions and departments in Canada can prepare for and adapt to infectious disease threats from a changing climate based on the experience and activities of the pilot regions. Conclusions:Through tool development by academic research and knowledge translation and transfer, public health capacity to anticipate and respond to emerging vector-borne and/or water-borne infectious diseases will be increased through the development of appropriate adaptation strategies.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".