Hand hygiene is crucial in preventing healthcare-associated infections. In this study, we aimed to quantify the accuracy of subjective evaluation of hand washing through visual inspection and objective evaluation through images. The participants were 24 consenting nursing students, and the study used black light and fluorescent paint to generate sketches and captured images of the unwashed areas, which were processed. Handwashing training was conducted once a week for four consecutive weeks. We collected data in the first and fourth training sessions. We found that the percentage of the unwashed palmar areas was significantly higher in the images than in the sketches (p<0.05). The percentage of the uncleaned area as recognized visually significantly increased for sketches (p=0.01) and decreased for images (p=0.009) in the fourth session. The difference between the percentages of the image and sketch area notably decreased in the fourth session (p=0.002). When we checked the recognition percentage of the unwashed area by dividing the right-hand palmar side into six areas, the fingertips had the highest percentage, and the ball of the thumb had the lowest percentage. The recognition of the unwashed areas was low when comparing the subjective visual assessment with the objective imagery assessment. In addition, the percentage of the unwashed areas decreased with repeated training, indicating a decrease in the difference between the subjective and objective ratings.

Effective infection control requires a close fit between the mask and face to minimize gaps. This study investigated whether surgical mask performance varies with face shape and mask size. Three facial models were 3D-printed using head-related transfer function data. Two mask sizes were tested on each model, and 3D measurements were taken at five facial points: the nose, cheeks, and chin to assess mask-to-face gaps. To simulate droplet emission, an aqueous sodium chloride solution was released from a pseudo-oral cavity in the models, and air leakage was measured using a mask-fitting tester. A two-way analysis of variance (ANOVA) was used to examine the effects of face and mask size on leakage. Small face models showed significantly higher leakage than medium and large ones (p < 0.001), and S-sized masks leaked more than M-sized masks regardless of face size (p = 0.038). Linear regression showed a positive correlation between chin gaps and leakage when using S-sized masks (p < 0.05). These results suggest that medium-sized masks offer better overall performance. However, for small faces, fit—especially at the chin, requires particular attention.