Occupational exposure monitoring of airborne respiratory viruses in outpatient medical clinics
Bibliographic record
Abstract
Exposure to airborne respiratory viruses can be a health hazard in occupational settings. In this study, air sampling was conducted from January to March 2023 in two outpatient medical clinics—one primary care clinic and one clinic dedicated to the diagnosis and treatment of respiratory illnesses—for the purpose of assessing airborne respiratory virus presence. Work involved the operation of a BioSpot-VIVASTM as a stationary air sampler and deployment of NIOSH BC-251 bioaerosol samplers as either stationary devices or personal air samplers worn by staff members. Results were correlated with deidentified clinical data from patient testing. Samples from seven days were analyzed for SARS-CoV-2, influenza A H1N1 and H3N2 viruses, and influenza B Victoria- and Yamagata-lineage viruses, with an overall 17.5% (17/97) positivity rate. Airborne viruses predominated in particles of aerodynamic diameters from 1–4 μm and were recovered in similar quantities from both clinics. BC-251 samplers (17.4%, 15/86) and VIVAS (18.2%, 2/11) collected detectable viruses at similar rates, but more numerous BC-251 samplers provided greater insight into virus presence across clinical spaces and job categories. 60% of samples from reception areas contained detectable virus, and exposure to significantly more virus (p = 0.0028) occurred at reception desks as compared to the “mobile” job categories of medical providers and nurses. Overall, this study provides valuable insights into the impacts of hazard mitigation controls tailored to reducing respiratory virus exposure and highlights the need for continued diligence toward exposure risk mitigation in outpatient medical clinics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".