Skin care and hygiene among healthcare professionals during and after the SARS-CoV-2 pandemic
Bibliographic record
Abstract
The severe acute respiratory syndrome coronavirus 2 pandemic has necessitated enhanced protection against viral transmission among healthcare professionals, particularly relating to handwashing and personal protective equipment. Some of these requirements may persist for years to come. They bring associated concerns around skin hygiene and general care, with damage to the face and hands now a well-documented consequence among healthcare professionals. This review assesses optimal skin care during the severe acute respiratory syndrome coronavirus 2 pandemic and in the "new normal" that will follow, identifies current knowledge gaps, and provides practical advice for the clinical setting. Regular, systematic hand cleaning with soap and water or an alcohol-based hand rub (containing 60%-90% ethanol or isopropyl alcohol) remains essential, although the optimal quantity and duration is unclear. Gloves are a useful additional barrier; further studies are needed on preferred materials. Moisturization is typically helpful and has proven benefits in mitigating damage from frequent handwashing. It may be best practiced using an alcohol-based hand rub with added moisturizer and could be particularly important among individuals with pre-existing hand dermatoses, such as psoriasis and eczema. Face moisturization immediately prior to donning a mask, and the use of dressings under the mask to reduce friction, can be helpful dermatologically, but more work is required to prove that these actions do not affect seal integrity. Nonetheless, such measures could play a role in institutional plans for mitigating the dermatologic impact of transmission control measures as we exit the pandemic.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".