Preparing for an Influenza Pandemic: Hospital Acceptance Study of Filtering Facepiece Respirator Decontamination Using Ultraviolet Germicidal Irradiation
Bibliographic record
Abstract
OBJECTIVES: Predictions estimate supplies of filtering facepiece respirators (FFRs) would be limited in the event of a severe influenza pandemic. Ultraviolet decontamination and reuse (UVDR) is a potential approach to mitigate an FFR shortage. A field study sought to understand healthcare workers' perspectives and potential logistics issues related to implementation of UVDR methods for FFRs in hospitals. METHODS: Data were collected at three hospitals using a structured guide to conduct 19 individual interviews, 103 focus group interviews, and 285 individual surveys. Data were then evaluated using thematic analysis to reveal key themes. RESULTS: Data revealed noteworthy variation in FFR use across the sample, along with preferences and requirements for the use of UVDR, unit design, and FFR reuse. Based on a scale of 1 (low) to 10 (high), the mean perception of safety in a high mortality pandemic wearing no FFR was 1.25 of 10, wearing an FFR for an extended period without decontamination was 4.20 of 10, and using UVDR was 7.72 of 10. CONCLUSIONS: In addition to technical design and development, preparation and training will be essential to successful implementation of a UVDR program. Ultraviolet decontamination and reuse program design and implementation must account for actual clinical practice, compliance with regulations, and practical financial considerations to be successfully adopted so that it can mitigate potential FFR shortages in a pandemic.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".