Climate Change and African Migrant Health
Bibliographic record
Abstract
INTRODUCTION: Climate change exacerbates existing sociopolitical and economic vulnerabilities, undermining livelihoods, inflating the risk of conflict, and making it difficult for people to remain stable. In 2019, around 25 million new displacements occurred due to natural disasters. This review aims to summarize the existing evidence regarding the impact of climate change on the health of African immigrants. METHODS: Nine databases were systematically searched using a strategy developed in collaboration with a subject librarian. Potentially relevant articles were identified, screened, and reviewed by at least two reviewers, with a third reviewer resolving conflicts where necessary. Data were extracted from relevant articles using a standardized form. RESULTS: Seven studies (three cross-sectional, two qualitative, one cohort, and one need assessment report) were identified; they included different categories of African migrants and reported on various aspects of health. The included articles report on climate change, e.g., flooding, drought, and excess heat, resulting in respiratory illness, mental health issues, malnutrition, and premature mortality among African immigrants. CONCLUSION: This review suggests climate change adversely affects the physical, mental, and social health of African immigrants. It also highlights a knowledge gap in evidence related to the impact of climate change on the health of African immigrants.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".