Strategies for managing N95 mask shortages at water resource recovery facilities during pandemics: a review
Bibliographic record
Abstract
As the numbers of COVID-19 cases grew globally, the severe shortages of health care respiratory protective equipment impacted the ability of water resource recovery facilities (WRRFs) to acquire N95 masks for worker protection. While the Occupational Safety and Health Administration (OSHA) encourages WRRFs to conduct job safety assessments to mitigate risks from bioaerosols, it does not provide clear guidance on respiratory protection requirements, leaving the use of N95 masks across the industry non-standardized and difficult to justify. Strategies need to be developed to cope with shortages during pandemics, and these should take into consideration a WRRF's size and disinfection equipment available. Our objective is to provide an overview of respiratory protection-related practices recommended for health care professionals that apply to WRRFs (e.g., elimination, substitution, extended use, reuse, disinfection). Reviewed N95 mask disinfection strategies included using hydrogen peroxide, autoclaving, moist heat, dry heat, ultraviolet germicidal irradiation (UVGI), ethylene oxide, chlorine and ethanol. Of these, dry heat, autoclaving and UVGI present the most promise for WRRFs, with UVGI being limited to larger utilities. We recommend that WRRFs work closely with disinfection technology manufacturers, mask providers, health and safety staff and inspectors to develop suitable programs to cope with N95 mask shortages during pandemics.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".