Respiratory Tract and Eye Symptoms in Wildland Firefighters in Two Canadian Provinces: Impact of Discretionary Use of an N95 Mask during Successive Rotations
Bibliographic record
Abstract
We examined whether discretionary use of an N95 mask reduced symptom reporting in wildland firefighters. The study collected data from two Canadian provinces during the 2021 fire season, with each firefighter followed for up to 4 rotations. Participants completed questionnaires on symptoms at the start and end of each rotation, when they reported also on mask use (if any) and completed a task checklist. Eighty firefighters contributed data. Nineteen firefighters were successfully fit-tested for N95 masks to wear whenever they felt conditions justified. Start-of-rotation symptoms reflected total hours firefighting in 2021. Symptoms of eye, nose and throat irritation and cough were more bothersome at the end of rotation. Cough, throat and nose (but not eye) symptoms were reported as significantly less bothersome at the end of rotation by those allocated masks, having allowed for crew type and start-of-rotation symptoms. Among those allocated a mask, use was most frequent during initial attack and least during driving and patrol. Reasons for not wearing included high work difficulty and low comfort. It is concluded that symptoms in wildland firefighters increased with hours of exposure. While provision of an N95 mask reduced symptoms, work is needed to overcome barriers to respiratory protection.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".