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Record W4402732979 · doi:10.1093/annweh/wxae073

A field investigation of 3 masks proposed as respiratory protection for wildland firefighters: a randomized controlled trial in British Columbia, Canada

2024· article· en· W4402732979 on OpenAlexafffundabout
Natasha Broznitsky, Mona Shum, David W. Kinniburgh, Drew Lichty, Sylvia Tiu, Taryn Toic, Tanis Zadunayski, Nicola Cherry

Bibliographic record

VenueAnnals of Work Exposures and Health · 2024
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of AlbertaUniversity of CalgaryInstitute of Indigenous Peoples' HealthGovernment of British Columbia
FundersUniversity of Alberta
KeywordsRespiratorPersonal protective equipmentEnvironmental scienceFirefightingParticulatesMedicineToxicologyChemistryCoronavirus disease 2019 (COVID-19)Internal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Wildland firefighters are exposed through the lungs and skin to particulate matter, fumes, and vapors containing polycyclic aromatic hydrocarbons (PAH). Wearing respiratory protection should reduce pulmonary exposure, but there is uncertainty about the most effective and acceptable type of mask. METHODS: Firefighters from 6 unit crews working with the British Columbia Wildfire Service were approached and those consenting were randomly allocated within each crew to a "no mask" control group or to use 1 of 3 types of masks: X, half-face respirator with P100/multi gas cartridge; Y, cloth with alpaca filter; Z mesh fabric with a carbon filter. Crews were followed for 3 consecutive firefighting days. The mask allocated was constant for each firefighter throughout. All participants completed a brief questionnaire at the start and end of each day, giving information on mask use, respiratory symptoms, and assessment of mask qualities. Spot urine samples were collected pre and post shift to assess 1-hydroxypyrene (1-HP) concentration as an indicator of total PAH absorption. Skin wipe samples from the hands and throat were collected pre and post shift and analyzed for PAH concentration. On each day monitored, 4 participants carried sampling pumps to measure total particulates and PAHs on particles and in vapor phase. The primary outcome was the concentration of urinary 1-HP at the end of the fire day. Secondary outcomes were changes in respiratory and eye symptoms during the course of the shift, reported mask use, and perception of mask qualities. The analysis used a 3-level random intercept regression model that clustered observations within individuals and crews. We aimed to detect any relation of allocated mask type to the 4 outcomes, having allowed for estimated exposure. RESULTS: Information was collected from 89 firefighters, including 14 women: 49% (37/75) of male firefighters were bearded. Nineteen fire days were monitored for a total of 263 firefighter × days, 64 to 68 for each intervention group. The end of shift 1-HP was higher than the start of the shift. Urinary 1-HP was more strongly related to PAHs on the skin than in the breathing zone. Men with beards had higher end-of-shift urinary log 1-HP/creat (ng/g) than other firefighters. None of the groups allocated a mask had lower 1-HP than the no-mask group, either in the study group overall or when stratified by beard-wearing. Among those without either beards or a failed fit-test, Mask Z reduced at the end of shift 1-HP where airborne PAH concentration was high. End-of-shift symptoms were related to particle mass in the breathing zone but was not mitigated by any of the masks. Hours electing not to wear a mask increased from the first to third shift for all mask types. Mask Z was rated as more comfortable than other types. Mask X was rated highest on fit and perceived protection. Mask Y gained the lowest ratings on fit, comfort and feelings of protection. CONCLUSIONS: Allocated masks did not provide protection overall, but the results highlighted the need for a wider understanding of the circumstances in which wearing efficient protection is well-advised.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.133
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.415
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2024
Admission routes3
Has abstractyes

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