Public Health Adaptation to Climate Change in OECD Countries
Bibliographic record
Abstract
Climate change is a major challenge facing public health. National governments play a key role in public health adaptation to climate change, but there are competing views on what responsibilities and obligations this will-or should-include in different nations. This study aims to: (1) examine how national-level public health adaptation is occurring in Organization for Economic Cooperation and Development (OECD) countries; (2) examine the roles national governments are taking in public health adaptation; and (3) critically appraise three key governance dimensions of national-level health adaptation-cross-sectoral collaboration, vertical coordination and national health adaptation planning-and identify practical examples suited to different contexts. We systematically reviewed publicly available public health adaptation to climate change documents and webpages by national governments in ten OECD countries using systematic web searches, assessment of self-reporting, and content analysis. Our findings suggest national governments are primarily addressing infectious disease and heat-related risks posed by climate change, typically emphasizing capacity building or information-based groundwork initiatives. We find national governments are taking a variety of approaches to public health adaptation to climate change that do not follow expected convergence and divergence by governance structure. We discuss practical options for incorporating cross-sectoral collaboration, vertical coordination and national health adaptation planning into a variety of contexts and identify leaders national governments can look to to inform their public health adaptation planning. Following the adoption of the Paris Agreement and subsequent increased momentum for adaptation, research tracking adaptation is needed to define what health adaptation looks like in practice, reveal insights that can be taken up across states and sectors, and ensure policy orientated learning.
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 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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".