Aerosol Box Use in Reducing Health Care Worker Contamination During Airway Procedures (AIRWAY) Study: Secondary Workload and Provider Outcomes in a Simulation-Based Trial*
Bibliographic record
Abstract
OBJECTIVES: An aerosol box aims to reduce the risk of healthcare provider (HCP) exposure to infections during aerosol generating medical procedures (AGMPs), but little is known about its impact on workload of team members. We conducted a secondary analysis of data from a prospective, multicenter, randomized controlled trial evaluating the impact of aerosol box use on patterns of HCP contamination during AGMPs. The objectives of this study are to: 1) evaluate the effect of aerosol box use on HCP workload, 2) identify factors associated with HCP workload when using an aerosol box, and 3) describe the challenges perceived by HCPs of aerosol box use. DESIGN: Simulation-based randomized trial, conducted from May to December 2021. SETTING: Four pediatric simulation centers. SUBJECTS: Teams of two HCPs were randomly assigned to control (no aerosol box) or intervention groups (aerosol box). INTERVENTIONS: Each team performed three scenarios requiring different pediatric airway management (bag-valve-mask [BVM] ventilation, laryngeal mask airway [LMA] insertion, and endotracheal intubation [ETI] with video laryngoscopy) on a simulated COVID-19 patient. National Aeronautics and Space Administration-Task Load Index (NASA-TLX) is a standard tool that measures subjective workload with six subscales. MEASUREMENTS AND MAIN RESULTS: A total of 64 teams (128 participants) were recruited. The use of aerosol box was associated with significantly higher frustration during LMA insertion (28.71 vs. 17.42; mean difference, 11.29; 95% CI, 0.92-21.66; p = 0.033). For ETI, there was a significant increase in most subscales in the intervention group, but there was no significant difference for BMV. Average NASA-TLX scores were all in the "low" range for both groups (range: control BVM 23.06, sd 13.91 to intervention ETI 38.15; sd 20.45). The effect of provider role on workloads was statistically significant only for physical demand ( p = 0.001). As the complexity of procedure increased (BVM → LMA → ETI), the workload increased in all six subscales ( p < 0.05). CONCLUSIONS: The use of aerosol box increased workload during ETI but not with BVM and LMA insertion. Overall workload scores remained in the "low" range, and there was no significant difference between airway provider and assistant.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".