Children and Climate Change Vulnerability Indices: a Scoping Review
Bibliographic record
Abstract
Abstract Extreme climate is increasingly causing distress. A greater understanding of how these hazards affect children is critical for informing further research and improving climate change adaptation and resilience. Identifying climate change vulnerability indices that assess the impact of extreme climate events on human health present in the scientific literature, only examining those that include children, is the main scope of this research. Literature from Ovid Medline, Embase, Web of Science, Environment Complete, and Greenfile published between database inception and 2022 was used to complete a two-stage process, first focusing on index and climate change inclusion criteria, then based on criteria such as child population and health impacts. Data extraction utilized Covidence. We extracted general information, methodology, study characteristics, findings, and children-specific variables for each paper. Consequently, 14 eligible studies were identified from 2,262 papers: two reported child-focused results regarding an association between climate events and health complications using indices. Most studies focused on children under 5 years old. The most common child-specific variables included in these indices were age, health variables, and immunization status. Heterogeneity in the index methodology was found. Ultimately, gaps in the upper age range of childhood, the utilization of different child-relevant variables, and geographical information were identified. Additional studies are required to expand our knowledge of child vulnerability to allow for more in-depth systematic literature reviews or to create an independent, comprehensive index directly addressing the child population in the context of local impacts of climate change to promote children's health.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".