Evidence-informed policy for tackling adverse climate change effects on health: Linking regional and global assessments of science to catalyse action
Bibliographic record
Abstract
ummary points• Effective policy making depends on synthesising and improving the use of existing robust scientific evidence, tackling misinformation, and identifying knowledge gaps to be filled by new research.• A global project organised by the InterAcademy Partnership (IAP) is bringing together evidence from Africa, Asia, the Americas, and Europe to evaluate climate change effects on health and to assess policy priorities for adaptation and mitigation solutions.Project design encouraged inclusivity in assessing research from across disciplines and from diverse geographical and socioeconomic contexts encompassing issues for vulnerable groups (including Indigenous Peoples) and integrating outputs at national, regional, and global levels.• Coordinated policy development approaches across sectors and regions and integration at national-regional-global levels are essential to understand trade-offs, avoid inadvertent consequences, and capitalise on potential synergies for multiple benefits for health, equity, and environment.• National priorities must include integrating health actions into national climate adaptation plans and Nationally Determined Contributions (NDCs) under the Paris Agreement.Regional policy action is important to address cross-boundary issues and to build critical mass for quantifying and implementing solutions.• A focus on human health can catalyse the strengthening of international coherence and commitment to tackling shared climate change challenges.Health must be prioritised in current global policy initiatives, including the United Nations Framework Convention on Climate Change (UNFCC) Conference of the Parties 26 (CAU : PleasenotethatCOP26hasbeendefined OP26), UN Convention on Biological Diversity (CBD) Conference of the Parties 15 (CAU : PleasenotethatCOP15hasbeendefined OP15), and the UN Food Systems Summit.The scientific and health communities have a key role to help lead efforts by engaging at the science-policy interfaces to address barriers to action.
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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 | 0.201 | 0.298 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.003 | 0.019 |
| Scholarly communication | 0.022 | 0.025 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".