Climate Change and Air Pollution: How Healthcare Providers Can Help Mitigate the Risks to Respiratory Health
Bibliographic record
Abstract
The intricate relationship between climate change and air pollution is having a significant impact on public health.Increases in global temperatures are leading to extreme weather events, changes in plant growth patterns, and higher levels of aeroallergens and air pollution; all of which can exacerbate pre-existing respiratory conditions and increase the risk of developing respiratory and other diseases.In this article, four world leaders in the field of respiratory health outline the evidence linking climate change and air pollution to poor respiratory health outcomes.They highlight that people living with lung conditions, such as asthma and chronic obstructive pulmonary disease (COPD), as well as pregnant people, children, older people, and those living in low-and middle-income countries (LMIC), are the most at risk.They emphasise the need for greater awareness among the public and healthcare professionals alike, talk about the role of healthcare teams in helping people to recognise and mitigate the risks, and share practical ways people can help to minimise the health impacts of climate change and pollution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".