Impact of climate change on child outcomes: an evidence gap map review
Bibliographic record
Abstract
BACKGROUND: Climate change and extreme weather events significantly threaten neonatal and child health. This review aims to provide a comprehensive overview of the current evidence on the impact of climate change on child health, using the evidence gap map (EGM) to address knowledge gaps and establish a foundation for evidence-based interventions and future research. METHOD: From inception, academic databases (such as MEDLINE, EMBASE, Global Health, CINAHL and Scopus) and grey literature were systematically searched. We included climate change-related studies involving children aged 0-5 worldwide. Covidence facilitated a rigorous screening process, and we conducted a critical appraisal. Two independent reviewers handled screening and data extraction. Eligible studies underwent coding and extraction using Evidence for Policy and Practice Information (EPPI) reviewer software. The EGM was constructed using EPPI Mapper, and comprehensive findings were presented through live links and figures. RESULT: We identified 196 studies, comprising 59.2% children and 40.8% neonates, with diverse research approaches, including 94% quantitative studies. There has been a notable increase in research publications over the past 5 years. Evidence is heavily concentrated in Asia (93 studies) and Africa (47 studies). The most frequently studied exposures are those related to extreme climate events, followed by drought and floods. However, there are gaps in the study of extreme cold and storms. The significant outcomes comprised preterm birth (55 studies), low birth weight (27 studies), malnutrition (59 studies) and diarrhoeal diseases (28 studies). Evidence on mental health problems and congenital disabilities receives relatively less attention. CONCLUSION: This EGM is crucial for researchers, policymakers and practitioners. It highlights knowledge gaps and guides future research to address the evolving threats of climate change to global child health. TRIAL REGISTRATION NUMBER: INPLASY202370086.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".