Initial findings from the implementation of a community-based sentinel surveillance system to assess the health effects of climate change in Alaska
Bibliographic record
Abstract
BACKGROUND: This report describes the results of a study to determine whether a community-based sentinel surveillance system can be developed and implemented to assess the health effects of climate change, and to contribute to local discussions to mitigate these health effects. The purpose of this report is to describe the process and outcomes of this innovative approach to identifying priority areas for adaptation investment. This report can be used to assist local, state and federal governments in determining how to develop actions and policies to promote adaptation to climate change. OBJECTIVE: To evaluate the health effects of climate change in rural Alaska. DESIGN: We conducted an iterative and participatory process to develop metrics, an instrument and a protocol to collect sentinel surveillance data on the health effects of climate change in 3 ecologically distinct regions of the state. RESULTS: We collected surveillance data from 91 study participants over the course of 12 months. These data were analyzed and categorized by frequency and association between specific health outcomes or health-related factors (such as food security) and reported exposure to environmental effects of climate change. We found significant associations between several health outcomes and health outcome mediators and reported exposures. We presented these data to study participants in community settings and moderated discussions of likely causal factors for these measured associations, and helped community residents to identify specific adaption measures to mitigate those health effects. CONCLUSIONS: We conclude that community-based sentinel surveillance is an effective method for assessing health outcomes from exposure to environmental effects of climate change, and informing climate change health adaptation planning in Alaskan communities. We contend that it would be effective in other regions of the nation as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".