Facemask and respirator use for bushfire smoke protection: A cross-country comparison of public health policies in Australia, Canada, India, and the United States
Bibliographic record
Abstract
BACKGROUND: As climate change intensifies the frequency and severity of bushfires, exposure to bushfire smoke is emerging as a significant public health concern, associated with numerous adverse health outcomes, including exacerbation of chronic obstructive pulmonary disease, asthma, cardiovascular diseases, and respiratory infections. OBJECTIVE: This study examined policies related to the use of masks and respirators as protective measures against smoke exposure. METHODS: Policies and guidelines of health departments, emergency and fire services, and other relevant organisations of selected countries were reviewed. Guidelines were sourced from organizational websites, PubMed, and Google Scholar using specific keywords. RESULT: There is variability in policies regarding mask and respirator use during bushfires. Health departments generally recommend using P2/ N95 respirators to protect the public from particulate exposure arising bushfire smoke, while emergency and fire services generally recommend surgical or cloth masks. Few guidelines provided detailed instructions on the proper use of respirators, including fit testing, or fit checking procedures. Most guidelines emphasised monitoring air quality and avoiding bushfire smoke, particularly for high-risk groups. There is no guidance provided on the length of time a mask should be used in any guideline. CONCLUSION: The inconsistent recommendations from health organisations and countries regarding mask and respirator use during bushfires highlights the lack of high-quality evidence in this area. Health, emergency and fire services, and other relevant organisations should provide clear guidance around types of facemasks, the length of time a facemask should be used and on proper use of respirators use, including training and fit checking.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".