Are nurses protected? Observing the risk of skin contamination when donning and doffing personal protective equipment, using a Canadian protocol.
Bibliographic record
Abstract
This study aimed to determine whether the Public Services Health and Safety Association’s \ndonning and doffing protocol for Ebola are effective in the prevention of skin and clothing \ncontamination. Ten third-year nursing students performed a donning and doffing simulation, \nwhich included donning personal protective equipment (PPE), being sprayed with GloGerm, \nperforming eight simulated movements, and doffing PPE. Fluorescent stains were observed \nusing an ultraviolet scan and were documented by their location and size. Four participants (N=4) \nexperienced at least one contamination event following the doffing of PPE. Contaminations were \nobserved on: the left dorsal lower leg (41.3mm; 64.0mm); the right dorsal lower leg (77.9mm); the \nleft plantar (9.5mm); the left index finger (2.8mm); the right middle finger (1.6mm); the left scapula \n(38.1mm); and the right buttock (57.2mm). Areas of difficulty in the protocol included donning and \ndoffing: the gown, N95 respirator, and the outer footwear. Failures with the equipment, including \nbreaches and punctures, also contributed to the documented contamination. Near-miss incidents were \nobserved in nine of the twenty-four steps in the protocols and occurred a total of twenty times. \nRevisions to the protocols were completed and included additional information for the following \nprotocol steps: hang hygiene, N95 respirator, gown, outer footwear, verification process.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".