Fit Testing Retrofitted Full-Face Snorkel Masks as a Form of Novel Personal Protective Equipment During the COVID-19 Pandemic
Bibliographic record
Abstract
OBJECTIVE: Bottlenecks in the personal protective equipment (PPE) supply chain have contributed to shortages of PPE during the coronavirus disease 2019 (COVID-19) pandemic, resulting in fractures in the functionality of health-care systems. This study was conducted with the aim of determining the effectiveness of retrofitted commercial snorkel masks as an alternative respirator for health-care workers during infectious disease outbreaks. METHODS: A retrospective analysis was performed, analyzing qualitative and quantitative fit test results of the retrofitted Aria Ocean Reef® full-face snorkeling mask on health-care workers at the McGill University Health Centre between April and June 2020. Historical fit test results, using medical-grade respirators, for health-care workers, were also analyzed. RESULTS: During the study period, 71 participants volunteered for fit testing, 60.6% of which were nurses. The overall fit test passing rate using the snorkel mask was 83.1%. Of the participants who did not previously pass fit testing with medical-grade respirators, 80% achieved a passing fit test with the snorkel respirator. CONCLUSIONS: The results suggest that this novel respirator may be an effective and feasible alternative solution to address PPE shortages, while still providing health-care workers with ample protection. Additional robust testing will be required to ensure that respirator fit is maintained, after numerous rounds of disinfection.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".