Direct and indirect health impacts of climate change on the vulnerable elderly population in East China
Bibliographic record
Abstract
The latest scientific advances on the impacts of climate change on the health of the elderly in East China were reviewed consulting peer-reviewed publications from 2000 to 2017. The direct impacts of climate change result from rising temperatures, heat waves, and increases in the frequency of complex extreme weather events such as windstorms, floods, and droughts. The health and social consequences of these events are far reaching, ranging from reduced labour productivity and heat-related deaths through to direct physical injury during extreme weather events, the spread of infectious diseases, and mental health effects following widespread flooding or prolonged drought. Research has indicated that climate change will have the greatest impact on vulnerable groups of people, including the elderly population. However, there is a dearth of empirical evidence, a lack of focus on vulnerable segments of the population (especially elderly), limited understanding of how health status will change in the future, and lack of acknowledgement of how different regions in China vary in terms of the consequences of climate change. The main risk in East China that climate change may exacerbate is flooding (sea level rise, coastal and riverine, flood risk). However in some regions of East China such as in the provinces of Anhui, Jiangsu, Hebei, and Shandong the biggest climate change risk is considered to be drought. Main health risks linked to climate change are evident as cardiovascular and respiratory diseases (heat stroke, exhaustion, and asthma), often caused by interactions between heat wave episodes and concurrent poor air quality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".