Impact of climate change on maternal health outcomes: An evidence gap map review
Bibliographic record
Abstract
Climate change poses unique challenges to maternal well-being and increases complications during pregnancy and childbirth globally. This evidence gap map (EGM) aims to identify gaps in existing knowledge and areas where further research related to climate change and its impact on maternal health is required. The following databases were searched individually from inception to present: Medline, EMBASE, and Global Health via OVID; Cumulative Index to Nursing and Allied Health Literature (CINAHL) via EBSCOhost; Scopus; and organizational websites. In this EGM, we integrated 133 studies published in English, including qualitative, quantitative, reviews and grey literature that examined the impact of climate change on maternal health (women aged 15-45). We used Covidence to screen studies and Evidence for Policy and Practice Information (Eppi reviewer)/Eppi Mapper software to generate the EGM. Data extraction and qualitative appraisal of the studies was done using critical appraisal tools. The study protocol was registered in International Platform of Registered Systematic Review and Meta-analysis Protocols (INPLASY) # INPLASY202370085. Out of 133 included studies, forty seven studies were of high quality, seventy nine moderate equality and seven low quality. This EGM found notable gaps in the literature regarding the distribution of research across regions. We found significant research in North America (51) and Asia (40 studies). However, Africa and the Caribbean had fewer studies, highlighting potential disparities in research attention and resources. Moreover, while the impact of extreme heat emerged as a prominent factor impacting maternal well-being, there is a need for further investigation into other climate-related factors such as drought. Additionally, while preterm stillbirth and maternal mortality have gained attention, there is an overlook of malnutrition and food insecurity indicators that require attention in future research. The EGM identifies existing research gaps in climate change and maternal health. It emphasizes the need for global collaboration and targeted interventions to address disparities and inform climate-responsive policies.
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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 | 0.023 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 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".