Identifying and Achieving Consensus on Health-Related Indicators of Climate Change in Nunavut
Bibliographic record
Abstract
Indigenous peoples of the North are affected by climate change, and future changes in climate are likely to continue to pose serious challenges. Climate change and the resulting change in the environment and communities are believed to further compound existing health issues. There is considerable regional variation within the circumpolar world, and each area of the Canadian Arctic has its own unique environmental and societal characteristics. Therefore, to track the impacts on human health in Nunavut, a monitoring framework—one that takes into account the territory’s unique context—must be implemented. The objective of this study was to identify human health indicators of climate change on a global scale with a focus on indicators relevant to the Canadian Arctic atmosphere, habitats, and peoples. The Piliriqatigiinniq Community Health Research Model provided the guiding framework for this exploratory study. First, a scoping review of health-related indicators of climate change was conducted. From this review, an initial list of 30 indicators was produced. Second, individuals from multiple sectors were invited to participate in a consensus-building process to identify health-related indicators of climate change for Nunavut. Through individual selection and group discussion, a final set of 20 indicators was chosen by workshop participants. The indicators identified in both phases focused on four key themes: 1) environmental health; 2) morbidity and mortality; 3) population vulnerability; and 4) mitigation, adaptation, and policy. Participants felt these indicators would be useful in practice in Nunavut. Next steps are to implement and monitor the utility of the selected indicators.
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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 | 0.150 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".