The Impact of Environmental Design on Doffing Personal Protective Equipment in a Healthcare Environment: A Formative Human Factors Trial
Bibliographic record
Abstract
OBJECTIVE To explore the impact of environmental design on doffing personal protective equipment in a simulated healthcare environment. METHODS A mixed-methods approach was used that included human-factors usability testing and qualitative questionnaire responses. A patient room and connecting anteroom were constructed for testing purposes. This experimental doffing area was designed to overcome the environmental failures identified in a previous study and was not constructed based on any generalizable hospital standard. RESULTS In total, 72 healthcare workers from Ontario, Canada, took part in the study and tested the simulated doffing area. The following environmental design changes were tested and were deemed effective: increasing prominence of color-coded zones; securing disinfectant wipes and hand sanitizer; outlining disposal bins locations; providing mirrors to detect possible contamination; providing hand rails to assist with doffing; and restricting the space to doff. Further experimentation and iterative design are required with regard to several important features: positioning the disposal bins for safety, decreasing the risk of contamination and user accessibility; optimal positioning of mirrors for safety; communication within the team; and positioning the secondary team member for optimal awareness. Additional design suggestions also emerged during this study, and they require future investigation. CONCLUSIONS This study highlights the importance of the environment on doffing personal protective equipment in a healthcare setting. Iterative testing and modification of the design of the environment (doffing area) are important to enhancing healthcare worker safety. Infect Control Hosp Epidemiol 2017;38:712-717.
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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 | 0.022 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".