Hand Hygiene among Health Care Workers during COVID-19 Pandemic: Challenges and Recommendations
Bibliographic record
Abstract
In-hospital transmission is one of the main routes of the 2019 novel coronavirus (SARS-CoV-2) spreading among health care workers (HCWs) who are the frontline fighters. However, coming into contact with COVID-19-positive patients is unavoidable. Therefore, hand hygiene is of utmost importance for the prevention of COVID-19 among HCWs. This purpose can be achieved by applying alcohol-based hand rubs, washing hands properly with soap and water, and applying other antiseptic agents. Nevertheless, regular hand hygiene could also be challenging, because water, detergents, and disinfectants may predispose HCWs to hand dermatitis. The current article reviews the risk factors for the development of hand dermatitis, with further focus on the most common agents used among HCWs. In addition, the risk of occupational hand dermatitis for each agent is evaluated to increase awareness of this common condition. Finally, some recommendations are discussed to reduce the effect of hand dermatitis on HCWs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".