Assessing Health Vulnerabilities and Adaptation to Climate Change: A Review of International Progress
Bibliographic record
Abstract
Climate change is increasing risks to human health and to the health systems that seek to protect the safety and well-being of populations. Health authorities require information about current associations between health outcomes and weather or climate, vulnerable populations, projections of future risks and adaptation opportunities in order to reduce exposures, empower individuals to take needed protective actions and build climate-resilient health systems. An increasing number of health authorities from local to national levels seek this information by conducting climate change and health vulnerability and adaptation assessments. While assessments can provide valuable information to plan for climate change impacts, the results of many studies are not helping to build the global evidence-base of knowledge in this area. They are also often not integrated into adaptation decision making, sometimes because the health sector is not involved in climate change policy making processes at the national level. Significant barriers related to data accessibility, a limited number of climate and health models, uncertainty in climate projections, and a lack of funding and expertise, particularly in developing countries, challenge health authority efforts to conduct rigorous assessments and apply the findings. This paper examines the evolution of climate change and health vulnerability and adaptation assessments, including guidance developed for such projects, the number of assessments that have been conducted globally and implementation of the findings to support health adaptation action. Greater capacity building that facilitates assessments from local to national scales will support collaborative efforts to protect health from current climate hazards and future climate change. Health sector officials will benefit from additional resources and partnership opportunities to ensure that evidence about climate change impacts on health is effectively translated into needed actions to build health resilience.
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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".