Development of a Climate Change Vulnerability Assessment Using a Public Health Lens to Determine Local Health Vulnerabilities: An Ontario Health Unit Experience
Bibliographic record
Abstract
Climate change is negatively impacting the health of Canadians and is accordingly expected to have a significant impact on public health agencies and their response to these health impacts throughout the twenty-first century. While national and international research and assessments have explored the potential human health impacts of climate change, few assessments have explored the implications of climate change from a local public health perspective. An applied research approach to expand local knowledge and action of health vulnerabilities through a climate change action plan and vulnerability assessment was utilized by a local public health agency. Adoption and adaptation of the approach used may be valuable for public health organizations to assist their communities. Through completing a vulnerability assessment, an evidentiary base was generated for public health to inform adaptation actions to reduce negative health impacts and increase resiliency. Challenges in completing vulnerability assessments at the local level include the framing and scoping of health impacts and associated indicators, as well as access to internal expertise surrounding the analysis of data. While access to quantitative data may be limiting at the local level, qualitative data can enhance knowledge of local impacts, while also supporting the creation of key partnerships with community stakeholders which can ensure climate action continues beyond the scope of the vulnerability assessment.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".