Educating families about the impacts of wildfire smoke on children’s health: opportunities for healthcare professionals
Bibliographic record
Abstract
A dramatic wildfire smoke season emerged in 2023.Chilean wildfire smoke blanketed communities across Chile and Argentina in February.In June, smoke covered parts of Scotland following fires in the Highlands while Canadian wildfires caused hazardous air conditions throughout North America.By August, damage from wildfires had broken multiple global records, devastating communities in Hawaii, the Canary Islands and the Mediterranean.The chance of adverse health events from wildfire smoke exposure increased even in populations previously considered less vulnerable.As trusted messengers, healthcare professionals are well positioned to advise parents about potential health consequences of wildfire smoke.Historically, paediatricians have not discussed such climate-related hazards during office visits. 1This absence of counselling may partially reflect paediatricians' selfperceived lack of knowledge about how to effectively communicate their harms.However, wildfire smoke is a growing global health hazard for children, and parents are increasingly turning to paediatricians for advice. 1 2 Children often spend more time outdoors than adults, breathe faster and take in more air relative to their body weight; their lungs are also still developing and maturing.Their nasal passages filter relatively less air pollution, allowing more particulate matter (PM) to penetrate deeper into their lungs. 2This is problematic because wildfire-smoke PM is more toxic than pollution from other sources (eg, traffic). 3Indeed, scientists continue to
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 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".