A systematic review of fit improvement strategies for respirators: lessons learned from the COVID-19 pandemic
Bibliographic record
Abstract
INTRODUCTION: The use of respirators and masks has increased dramatically during outbreaks of respiratory infections, such as the COVID-19 pandemic. Both filtration efficiency and respirator fit testing influence the provision of effective respiratory protection to users. If healthcare workers (HCWs) do not have access to tight-fitting N95 filtering facepiece respirators (FFRs) or if fit testing procedures are not feasible, some cost‒benefit fit improvement strategies (FISs) could benefit HCW respiratory protection against respiratory infection pandemics. OBJECTIVE: The objective of this systematic review is to investigate the importance of fit testing and to identify the optimal factors influencing respirator or mask fit characteristics, particularly in emergency situations. METHODS: We searched four databases, including PubMed, Scopus, Web of Science, and Science Direct from February 5, 2020, to December 7, 2024, covering the COVID-19 pandemic period. Finally, a gray literature search was conducted to ensure that no further studies were missed. Additionally, quality assessment of the included studies was performed according to the Newcastle-Ottawa Scale. RESULTS: A total of 39 full texts were included in the systematic review. Seven categories of FISs included fitters or braces, double masking with cloth or medical masks over FFRs, ear loop knotting and tucking or using ear guards (hooks, clips), adhesive tape, skin protectants/dressings, wearing goggles over FFRs, and using cloths over facial hair to improve fit. Each FIS has its own advantages and disadvantages. Overall, there was an improvement in fitting after the application of the FISs. CONCLUSIONS: Among all, mask frame, ear loop strap modification, medical tape, thin dressings, double masking, and goggles donning modification are considered as pleasant FISs during performing the occupational activity. Among all, the mask frame and medical tape outperformed the other FISs. It is crucial that all respirators modified with FISs undergo standard fit testing procedures to avoid a false sense of security and prevent exposure to hazardous respiratory substances. Both safety and ergonomic factors are of great importance when applying each FIS.
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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 | 0.018 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".