Tools and Methods to Include Health in Climate Change Adaptation and Mitigation Strategies and Policies: A Scoping Review
Bibliographic record
Abstract
Climate change represents a serious threat to the health and well-being of populations. Today, many countries, regions, and cities around the world are implementing policies and strategies to adapt to climate change and mitigate its effects. A scoping review was performed to identify tools and methods that help integrate health into climate change adaptation and mitigation policies and strategies. The literature search includes scientific and grey literature. The scientific literature was conducted using PubMed, Elsevier Embase, and Web of Science databases. A grey literature web search was performed to complement the results. A total of 35 studies (28 from the scientific literature and 7 from the grey literature) were finally included. A large majority of research articles (24/28) and almost all reports (6/7) from the grey literature were published after 2010. Results show that the tools that were found most frequently are the nested models (12/35), health impact assessment (6/35), vulnerability and adaptation assessment (3/35), conceptual frameworks (3/35), and mixed methods (3/35). This review shows an increasing interest in the topic of developing tools to better manage health issues in adaptation and mitigation strategies, with a recent increase in the number of publications. Additional analyses of tools' effectiveness should be conducted in further studies.
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 | 0.044 | 0.104 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.036 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| 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".