Personal Protective Equipment for Infectious Disease Preparedness: A Human Factors Evaluation
Bibliographic record
Abstract
OBJECTIVE To identify issues during donning and doffing of personal protective equipment (PPE) for infectious diseases and to inform PPE procurement criteria and design. DESIGN A mixed methods approach was used. Usability testing assessed the appropriateness, potential for errors, and ease of use of various combinations of PPE. A qualitative constructivist approach was used to analyze participant feedback. SETTING Four academic health sciences centers: 2 adult hospitals, 1 trauma center, and 1 pediatric hospital, in Toronto, Canada. PARTICIPANTS Participants (n=82) were representative of the potential users of PPE within Western healthcare institutions. RESULTS None of the tested combinations provided a complete solution for PPE. Environmental factors, such as anteroom layout, and the design of protocols and instructional material were also found to impact safety. The study identified the need to design PPE as a complete system, rather than mixing and matching components. CONCLUSIONS Healthcare institutions are encouraged to use human factors methods to identify risk and failure points with the usage of their selected PPE, and to modify on the basis of iterative evaluations with representative end users. Manufacturers of PPE should consider usability when designing the next generation of PPE. Infect Control Hosp Epidemiol 2016;37:1022-1028.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".