Survey of Hand Hygiene, High-Touch Device Use, and Proper Habits of Health Care Workers for Infection Risk Prevention: Protocol for a Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Care-related infections are infectious diseases that occur in a care setting. The most important prevention methods are hand hygiene and the proper use of gloves and gowns. Recent literature points out that hand contact with mobile devices or potentially contaminated environments can promote an increased occurrence of health care-associated infections (HAIs). OBJECTIVE: This study aims to analyze the correlation between the microbial population present on the hands of health care professionals in the wards of the Tor Vergata Hospital in Rome and the microorganisms present on the surfaces of their smartphones and tablets by searching for the main agents responsible for HAIs. METHODS: Sterile swabs will be used to collect samples from hands and smartphones, which will then be plated on nutrient agar and other selective media for microbial count. Colonies showing growth and morphological characteristics suggestive of potential pathogens will be isolated and subjected to further analysis for identification at the species level and for antimicrobial resistance profiling (using a proprietary automated analytical profile index system). Sampling will be conducted quarterly (first 2 weeks of each quarter) to assess any changes in microbial flora. In parallel, an Italian version of the World Health Organization questionnaire on health care workers' knowledge of hand hygiene and a questionnaire on the use of high-touch devices will be administered to participants. Each quarter, 30 health workers will be selected, resulting in a total of 120 health workers and 240 samples collected by the end of the study. For each sample, the analysis will focus on quantifying the total bacterial load at 37 °C and 22 °C, along with detecting coliforms or Escherichia coli, Enterococci, Staphylococci, Acinetobacter, Klebsiella, Pseudomonas, and any associated antimicrobial resistance. RESULTS: The study aims to begin sample collection by June 2025. The protocol was properly evaluated and approved by the territorial ethics committee "Lazio Area 2" on March 21, 2024, with the code 76.24 CET2 utv_ptv. CONCLUSIONS: The findings of this study will be crucial in highlighting the need for targeted education and training of health care practitioners involved in the study, with a focus on the prevention of HAIs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60450.
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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 | 0.014 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".