Climate Change and Child Health Inequality: A Review of Reviews
Bibliographic record
Abstract
There is growing evidence on the observed and expected consequences of climate change on population health worldwide. There is limited understanding of its consequences for child health inequalities, between and within countries. To examine these consequences and categorize the state of knowledge in this area, we conducted a review of reviews indexed in five databases (Medline, Embase, Web of Science, PsycInfo, Sociological Abstracts). Reviews that reported the effect of climate change on child health inequalities between low- and high-income children, within or between countries (high- vs low-middle-income countries; HICs and LMICs), were included. Twenty-three reviews, published between 2007 and January 2021, were included for full-text analyses. Using thematic synthesis, we identified strong descriptive, but limited quantitative, evidence that climate change exacerbates child health inequalities. Explanatory mechanisms relating climate change to child health inequalities were proposed in some reviews; for example, children in LMICs are more susceptible to the consequences of climate change than children in HICs due to limited structural and economic resources. Geographic and intergenerational inequalities emerged as additional themes from the review. Further research with an equity focus should address the effects of climate change on adolescents/youth, mental health and inequalities within countries.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".