Climate change impacts on the health of South Asian children and women subpopulations - A scoping review
Bibliographic record
Abstract
Background and objectives: Climate change impacts are felt unequally worldwide; populations that experience geographical vulnerability, those living in small island states and densely populated coastal areas, and children and women are affected disproportionately. This scoping review aims to synthesize evidence from relevant studies centred on South Asia, identify research gaps specifically focused on children and women's health, and contribute to knowledge about South Asia's existing mitigation and adaptation strategies. Methods: A research librarian executed the search on six databases using controlled vocabulary (e.g., MeSH, Emtree, etc.) and keywords representing the concepts "vulnerable populations" and "climate change" and "health impacts" and "South Asia." Databases were searched from January 2010 to May 2020. Papers were screened independently by two researchers. Results: Forty-two studies were included, of which 23 were based in India, 14 in Bangladesh, and five in other South Asian countries. Nineteen studies focused on meteorological factors as the primary exposure. In contrast, thirteen focused on extreme weather events, nine on air pollution, and one on salinity in coastal areas. Thirty-four studies focused on the health impacts on children related to extreme weather events, meteorological factors, and air pollution, while only eight studies looked at health impacts on women. Undernutrition, ARI (acute respiratory infection), diarrheal diseases, low birth weight, and premature mortality were the major health impacts attributed to extreme weather events, meteorological factors, and air pollution exposure in children and women in the region. Conclusion: Extreme weather events, meteorological factors and air pollution have affected the health of children and women in South Asia. However, the gap in the literature across the South Asian countries concerning relationships between exposure to extreme weather events, meteorological factors, air pollution and health effects, including mental health problems in children and women, are opportunities for future work.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".