Priority climate and health modelling needs
Bibliographic record
Abstract
Climate and health modelling is necessary for improving understanding of the current and future distribution and timing of climate-related health risks. However, underinvestment in this area has limited the understanding required to inform policies that enable multisectoral interventions to safeguard health. We synthesised insights from a survey of 65 global climate and health modelling experts and 36 participants in a hybrid meeting to identify priority strategies for enhancing the validity, utility, and policy relevance of climate and health models. Foundational investments to support modelling included strengthening research capacity, establishing a network of multinational centres of excellence for transdisciplinary research and capacity building, improving data collection and sharing infrastructure, investing in scenario development and quantitative elaboration, assessing adaptation effectiveness, and committing to intermodel comparisons and interdisciplinary modelling activities. Specific recommendations included updating the 2014 WHO Quantitative Risk Assessment to cover a wider range of causal pathways and health endpoints, using interdisciplinary methods that facilitate model intercomparisons. Additional recommendations included supporting modelling of a broader set of climate-health outcomes, developing models to support early warning systems and investments in their implementation, evaluation, and maintenance, and improving health system capacity for modelling in low-resource settings.
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 | 0.043 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".