The need for community-led, integrated and innovative monitoring programmes when responding to the health impacts of climate change
Bibliographic record
Abstract
In Northern Canada, climate change has led to many acute and interrelated health and environmental impacts experienced among Inuit populations. Community-based monitoring, in which community members participate in monitoring initiatives using various forms of technology, is a key strategy increasingly used to detect, monitor and respond to climate change impacts. To better understand the landscape of existing environmental and health monitoring programmes mobilising different technologies and operating in the North we conducted a review that used environmental scan methodologies to explore and contextualise these programmes. We consulted with academic researchers with experience in community-led monitoring, conducted systematic searches of grey and peer-reviewed literature, and conducted a secondary search for environment-health mobile-phone applications. Following specific criteria, we identified 18 monitoring programmes using information and communication technologies in the North, and three global monitoring mobile-phone applications, which cumulatively monitored 74 environment and health indicators. Several themes emerged, including the need for: (1) community leadership, (2) indicators of environment and/or human health and (3) innovative technology. This synthesis supports the development of community-led, environment-health monitoring programmes that use innovative technology to monitor and share information related to the health implications of climate change in and around Indigenous communities throughout the Circumpolar North.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".