Responding to Climate and Environmental Change Impacts on Human Health via Integrated Surveillance in the Circumpolar North: A Systematic Realist Review
Bibliographic record
Abstract
Environments are shifting rapidly in the Circumpolar Arctic and Subarctic regions as a result of climate change and other external stressors, and this has a substantial impact on the health of northern populations. Thus, there is a need for integrated surveillance systems designed to monitor the impacts of climate change on human health outcomes as part of broader adaptation strategies in these regions. This review aimed to identify, describe, and synthesize literature on integrated surveillance systems in Circumpolar Arctic and Subarctic regions, that are used for research or practice. Following a systematic realist review approach, relevant articles were identified using search strings developed for MEDLINE® and Web of Science™ databases, and screened by two independent reviewers. Articles that met the inclusion criteria were retained for descriptive quantitative analysis, as well as thematic qualitative analysis, using a realist lens. Of the 3431 articles retrieved in the database searches, 85 met the inclusion criteria and were analyzed. Thematic analysis identified components of integrated surveillance systems that were categorized into three main groups: structural, processual, and relational components. These components were linked to surveillance attributes and activities that supported the operations and management of integrated surveillance. This review advances understandings of the distinct contributions of integrated surveillance systems and data to discerning the nature of changes in climate and environmental conditions that affect population health outcomes and determinants in the Circumpolar North. Findings from this review can be used to inform the planning, design, and evaluation of integrated surveillance systems that support evidence-based public health research and practice in the context of increasing climate change and the need for adaptation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".