To wear or not to wear? The influence of regulatory focus and individual cultural orientation on face mask wearing

During the COVID-19 pandemic, much focus has been placed on the healthcare benefits of wearing face masks, yet some people resist wearing them. Though mask mandates may enhance face mask adoption in the short run, the effectiveness of such mandates, in the long run, remains questionable. Thus, understanding of psychological and sociological mechanisms behind wearing face masks becomes pertinent. This study by examining these underlying mechanisms, tends to answer two research questions: (1) How does regulatory focus impact one's behavior to wear face masks? (2) How does the impact of regulatory focus on this behavior vary under different cultural orientations? Drawing on the theory of regulatory fit, we found that people with a prevention focus will have fewer concerns about wearing face masks than people with a promotion focus. In addition, we also found that prevention-focused people who exhibited a cultural orientation with higher levels of collectivism, masculinity, power distance, and uncertainty avoidance had fewer concerns about face mask wearing perception and were more likely to wear face masks than did promotion-focused people with the same cultural orientation. The implications of these findings on the relevant literature and practice are also discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Behavioral changes of preventive activities of influenza among children in satellite cities of a metropolitan area of Tokyo, Japan, by the COVID-19 pandemic.

OBJECTIVE: In children in a metropolitan area of Tokyo, Japan, behavioral change and influenza infection associated with the frequency of nonpharmaceutical interventions (NPI) was assessed from the 2018-2019 season (Preseason) and the 2020-2021 season (coronavirus disease 2019 [COVID-19] season). METHODS: We conducted an exclusive survey among children attending preschool, elementary school, and junior high school in the Toda and Warabi regions, Japan, during the 2018-2019 (Preseason, distributed via mail) and 2020-2021 seasons (COVID-19 season, conducted online). The proportion of preventive activities (hand washing, face mask-wearing, and vaccination) was compared in the Preseason with that of the COVID-19 season. The multivariate logistic regression model was further applied to calculate the adjusted odds ratio (AOR) with 95% confidence intervals (CIs) for influenza infection associated with NPI frequency (hand washing and face mask wearing) in each Preseason and COVID-19 season. RESULTS: The proportion of vaccinated children who carried out hand washing and face mask wearing was remarkably higher during the COVID-19 season (48.8%) than in the Preseason (18.2%). A significant influenza infection reduction was observed among children who washed hands and wore face masks simultaneously (AOR, 0.87; 95% CI, 0.76-0.99; P = 0.033). CONCLUSIONS: A strong interest and performance in the intensive measures for the prevention of influenza under the COVID-19 pandemic was demonstrated. Positive association was observed from a combination of NPI, hand washing, and face mask-wearing and influenza infection. This study's findings could help in activities or preventive measures against influenza and other communicable diseases in children.

REAL-TIME FACE DETECTORS AND CLASSIFICATION PERFORMANCE IMPLEMENTING RESNET-50 MODEL FOR FACE MASK-WEARING CONDITIONS

The World Health Organization requires the community to wear a face mask to avoid transmission of COVID-19. The study investigates the performance of face detectors and evaluates the classification performance based on face mask-wearing conditions. The study built a total of 13,806 datasets that recorded an overall classification performance of 98%. The findings show that Multi-task Cascade Convolutional Neural Networks outperformed the other face detectors with an average score of 70% in accordance to distance, angles, occlusions, and multiple detections across given set conditions. Furthermore, the model recorded an accuracy performance of 83% for "correct wearing of face mask", 91% for "incorrect wearing of face mask", and 95% for "no face mask". However, despite the promising performance rates, the identified best face detector decreases when the given conditions are set to a higher level. To further improve and optimize the face mask-wearing conditions, the study highly recommends employing both statistical and mathematical analysis. © School of Engineering, Taylor's University.

YoloMask: An Enhanced YOLO Model for Detection of Face Mask Wearing Normality, Irregularity and Spoofing

Wearing of surgical face masks has become the new norm of our daily life in the context of the COVID-19 pandemic. Under many conditions at various public places, it is necessary to check or monitor whether the face mask is worn properly. Manual judgement of mask wearing not only wastes manpower but also fails to monitor it in a way of all-time and real-time, posing the urge of an automatic mask wearing detection technology. Earlier automatic mask wearing methods uses a successive means in which the face is detected first and then the mask is determined and judged followingly. More recent methods take the end-to-end paradigm by utilizing successful and well-known CNN models from the field of object detection. However, these methods fail to consider the diversity of face mask wearing, such as different kinds of irregularity and spoofing. Thus, we in this study introduce a comprehensive mask wearing detection dataset (named as Diverse Masked Faces) by distinguishing a total of five different classes of mask wearing. We then adapt the YOLOX model for our specific task and further improve it using a new composite loss which merges the CIoU and the alpha-IoU losses and inherits both their advantages. The improved model is referred as YoloMask. Our proposed method was tested on the new dataset and has been proved to significantly outperform other SOTA methods in the literature that are either successive or end-to-end. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Face Mask Wearing Recognition System for Big Data Video Streaming

With the outbreak of the covid-19 pandemic in recent years, Video Stream Analytics technology quickly became a hot topic of discussion across technology forums. As it has appeared, in the pandemic situation in recent years, the use of masks when interacting with the community is a must, that's why the research works on mask identification today and more. receiving more and more attention. Understanding the situation, the team conducted facial recognition analysis inside the video to determine if the people appearing in the video were wearing masks. to then apply the trained model into practice. After a period of research, the team has also successfully built a mask recognition system that can generate images and can display the results as real-time video. Especially, the model is trained successful using systemml machine learning system. This is considered a positive result with real-time masked face recognition analysis. © 2022 IEEE.

An Efficient Face Mask Wearing Detection Algorithm Based on Improved YOLOv3

In recent years, the Corona Virus Disease 2019 (Covid-19) epidemic has raged around the world, with more than 500 million people diagnosed. Relevant medical research and analysis results on Covid-19 indicate that wearing masks is an effective method to prevent and restrain virus transmission. Mask detection stations have been set up in hospitals, railway stations, schools, where there is large crowd flow, but results are not as good as expected. In order to ameliorate pandemic preventing and control measures, a mask wearing detection algorithm YOLOv3-M3 was designed and proposed in this paper. The algorithm can effectively detect people without mask, while consequently reminding them. Firstly, we substituted the feature extraction network of YOLOv3 with MobileNetv3, a lightweight convolutional neural network. Secondly, we utilized K-Means++ to substitute the original ground truth clustering algorithm to improve prediction precision. In addition, the bounding box regression loss function was revised as CIoU loss function. This loss function solves the issues of overlapping between the ground truth and the anchor box, which has increased the training speed. After experiments, the precision of YOLOv3 algorithm on mAP 0.5 and mAP 0.75 is 93.5% and 71.9%, respectively. Elevating 3.1% and 2.6%, respectively, higher than that of YOLOv3 algorithm, and it was superior to SSD, SSD Lite, YOLOv3-Tiny and other one-stage object detection algorithms. The detection speed can reach 13.6 frame/s, which has met the requirements of pandemic prevention and control in most places and can be deployed on terminal devices for object detection. [ FROM AUTHOR]

Strong social ties and adherence to face mask wearing in COVID-19 pandemic in Dagestan, North Caucasus

Is there a cause-and-effect relationship between the application of the personal protection equipment and strong social ties? We look at face-masks wearing in Dagestan republic in southern Russia. The social context of Covid-19 in Russia has not been exhaustively analyzed yet and medical landscapes in the post-Soviet context differ significantly from the Western models. We believe that such artifacts as face-masks are good for tracing relations between people, the virus, and the state. Contrary to the research based on data from the United States and China, our research reveals that there is not necessarily a cause-and--effect relationship between mask wearing and strong social ties. Face masks in Dagestan never became embodied artifacts despite strong social ties in the republic. Cultural and political context needs to be considered when thinking about the relationship between the strength of social ties and application of PPE. © 2022, Polska Akademia Nauk. All rights reserved.

EfficientMask-Net for face authentication in the era of COVID-19 pandemic.

Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, such as mobile and embedding models with low-memory requirements. The method identifies face mask-wearing conditions in two different schemes: I. Correctly Face Mask (CFM), Incorrectly Face Mask (IFM), and Not Face Mask (NFM) wearing; II. Uncovered Chin IFM, Uncovered Nose IFM, and Uncovered Nose and Mouth IFM. The proposed method can also be helpful to unmask the face for face authentication based on unconstrained 2D facial images in the wild. In this study, deep convolutional neural networks (CNNs) were employed as feature extractors. Then, deep features were fed to a recently proposed large margin piecewise linear (LMPL) classifier. In the experimental study, lightweight and very powerful mobile implementation of CNN models were evaluated, where the novel "EffientNetb0" deep feature extractor with LMPL classifier outperformed well-known end-to-end CNN models, as well as conventional image classification methods. It achieved high accuracies of 99.53 and 99.64% in fulfilling the two mentioned tasks, respectively.

COVID-19 Preventive Behaviors and Influencing Factors in the Thai Population: A Web-Based Survey.

Objectives: To identify factors influencing COVID-19 preventive behaviors among the Thai population. Methods: A cross-sectional web-based survey was used. A total of 6,521 Thai people completed the survey. The multiple linear regression analysis was performed to identify factors that influenced coronavirus disease 2019 (COVID-19) preventive behaviors. The Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation (PRECEDE) model was applied to propose factors influencing COVID-19 preventive behaviors. Results: The factors that mostly influenced COVID-19 prevention behaviors when controlling for the other variables are social support (ß = 0.173, p < 0.001) follow by age (ß = 0.162, p < 0.001), flu-like symptoms (ß = 0.130, p < 0.001), gender (ß = -0.084, p < 0.001), perceived risk of exposure (ß = 0.035, p < 0.05), lock down policy (ß = 0.029, p < 0.05), and residential area (ß = -0.027, p < 0.05), respectively. These factors explained 52% of the COVID-19 preventive behaviors in Thai population. Conclusion: The result of this study was a foundation for further studies on different groups of people to develop different strategies to adopt preventive behaviors to reduce the spread of the COVID-19.

Contextual Factors of Face Mask Wearing During the COVID-19 Pandemic and Recommendations to Increase Compliance Rate.

Community-wide face mask wearing is recognised as an effective non-pharmaceutical defence against infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative virus of coronavirus disease 2019 (COVID-19) pandemic. However, few studies have identified contextual factors of face mask wearing during the COVID-19 pandemic. This study aims to identify relationships between demographic factors, personal hygiene factors, online behavioural factors and face mask wearing by Malaysian adults during the COVID-19 pandemic. Data were collected via an online survey questionnaire and analysed with Statistical Package for Social Sciences version 26. Non-availability of personal protective equipment (PPE) as well as fewer social media hours and fewer hours of browsing information related to the COVID-19 pandemic were identified as factors related to low compliance rate of face mask wearing by some Malaysian adults. This study advances contextual understanding of face mask wearing by specific groups during the COVID-19 pandemic and puts forth several recommendations to increase face mask wearing compliance rate.

FCOSMask: Fully Convolutional One-Stage Face MaskWearing Detection Based on MobileNetV3

method against the worldwide Coronavirus disease 2019 (COVID- 19). This paper proposes FCOSMask, a fully convolutional one-stage face mask wearing detector based on the lightweight network, for emergency epidemic control and long-term epidemic prevention work. MobileNetV3 is applied as the backbone network to reduce computational overhead. Thus, complex calculation related to anchor boxes is avoided in the anchor-free method, and Complete Intersection over Union (CIoU) loss is selected as the bounding box regression loss function to speed up model convergence. Experiments show that compared to other anchor-based methods, detection speed of FCOSMask is improved around 3 to 4 times on self-established datasets and mean average precision (mAP) achieves 92.4%, which meets the accuracy and real-time requirements of the face mask wearing detection task in most public areas. Finally, a Web-based face mask wearing system is developed that can support public epidemic prevention and control management.. © 2021 Association for Computing Machinery. All rights reserved.

Modeling Effects of Spatial Heterogeneities and Layered Exposure Interventions on the Spread of COVID-19 across New Jersey.

COVID-19 created an unprecedented global public health crisis during 2020-2021. The severity of the fast-spreading infection, combined with uncertainties regarding the physical and biological processes affecting transmission of SARS-CoV-2, posed enormous challenges to healthcare systems. Pandemic dynamics exhibited complex spatial heterogeneities across multiple scales, as local demographic, socioeconomic, behavioral and environmental factors were modulating population exposures and susceptibilities. Before effective pharmacological interventions became available, controlling exposures to SARS-CoV-2 was the only public health option for mitigating the disease; therefore, models quantifying the impacts of heterogeneities and alternative exposure interventions on COVID-19 outcomes became essential tools informing policy development. This study used a stochastic SEIR framework, modeling each of the 21 New Jersey counties, to capture important heterogeneities of COVID-19 outcomes across the State. The models were calibrated using confirmed daily deaths and SQMC optimization and subsequently applied in predictive and exploratory modes. The predictions achieved good agreement between modeled and reported death data; counterfactual analysis was performed to assess the effectiveness of layered interventions on reducing exposures to SARS-CoV-2 and thereby fatality of COVID-19. The modeling analysis of the reduction in exposures to SARS-CoV-2 achieved through concurrent social distancing and face-mask wearing estimated that 357 [IQR (290, 429)] deaths per 100,000 people were averted.

Pandemic Worry and Preventive Health Behaviors During the COVID-19 Outbreak.

Objective: As schools are preparing for onsite learning, it is urgently needed to characterize the extent of pandemic worry and to examine predictors of adopting preventive health behaviors of hand washing, face mask wearing, and maintaining social distance among student pharmacists. Methods: An online survey was sent to 326 student pharmacists in the United States. Pandemic worry was measured using a seven-point Likert scale ranging from extremely not afraid of, to extremely afraid of getting COVID-19. The health belief model (HBM) was the theoretical framework of this study. Preventive health behaviors and components of the HBM were also measured using seven-point Likert scales (one indicated extremely unlikely; seven indicated extremely likely). Multivariable linear regression models were used to identify predictors of each behavior. Results: A medium level of pandemic worry (M = 4.2, SD = 1.92) was identified and females reported a higher pandemic worry. Respondents reported that they were extremely likely to wash their hands (M = 6.8, SD = 0.48) and maintain social distance (M = 6.6, SD = 0.92), but were moderately unlikely to wear face masks (M = 2.2, SD = 1.51). Determinants of face mask wearing included pandemic worry, perceived benefits, cue to action, self-efficacy, and being of an Asian American. Perceived barriers were negatively associated with face mask wearing. Conclusion: Strategies should be implemented to reduce the psychological impact of COVID-19 pandemic among student pharmacists. Predictors identified in this study should be incorporated in efforts to improve face mask wearing. Continued monitoring of pandemic worry and preventive health behaviors is of great significance when universities and colleges are for onsite learning.

Face Mask Wearing Behaviors, Depressive Symptoms, and Health Beliefs Among Older People During the COVID-19 Pandemic.

The COVID-19 pandemic has affected more than 100 countries. Despite the global shortage of face masks, the public has adopted universal mask wearing as a preventive measure in many Asian countries. The COVID-19 mortality rate is higher among older people, who may find that wearing a face mask protects their physical health but jeopardizes their mental health. This study aimed to explore the associations between depressive symptoms, health beliefs, and face mask wearing behaviors among older people. By means of an online survey conducted between March and April 2020, we assessed depressive symptoms, health beliefs regarding COVID-19, and face mask use and reuse among community-dwelling older people. General linear models were employed to explore the associations among these variables. Of the 355 valid participants, 25.6% experienced depressive symptoms. Health beliefs regarding the perceived severity of disease (p = 0.001) and perceived efficacy of practicing preventive measures (p = 0.005) were positively associated with face mask use. Those who reused face masks (p = 0.008) had a stronger belief in disease severity (p < 0.001), had poorer cues to preventive measures (p = 0.002), and were more likely to experience depressive symptoms. Mask reuse was significantly associated with depression only among those who perceived the disease as serious (p = 0.025) and those who had poorer cues to preventive measures (p = 0.004). In conclusion, health beliefs regarding perceived severity and efficacy contributed to more frequent face mask use, which was unrelated to depressive symptoms. Older people who had a stronger belief in disease severity had less adequate cues to preventive measures and reused face masks experienced greater depressive symptoms. A moderation effect of health beliefs (i.e., disease severity and cues to preventive measures) on face mask reuse and depression was observed.

Gender, face mask perceptions, and face mask wearing: Are men being dangerous during the COVID-19 pandemic?

Recent popular press authors have proposed that men are less likely to wear face masks during the COVID-19 pandemic. We investigate this notion in the current article by analyzing three extant datasets. We also assess the mediating effect of eight different face mask perceptions in the relation between gender and face mask wearing via the Face Mask Perceptions Scale. Across the three datasets, the sample-size weighted meta-analytic correlation between gender and face mask wearing was not statistically significant, and no face mask perception was a consistent mediator of this effect. Gender did have significant relations with two face mask perceptions, however. Men were more likely to perceive face masks as infringing on their independence, whereas women were more likely to perceive face masks as uncomfortable. Therefore, although gender does not relate to whether a person wears a face mask, it does relate to face mask perceptions. We offer several suggestions for research and practice from these results, such as the positioning of face mask wearing alongside passive health behaviors, the broader study of face mask perceptions' outcomes beyond face mask wearing, as well as the creation of interventions to target differing face mask perceptions across genders.

Understanding face mask use to prevent coronavirus and other illnesses: Development of a multidimensional face mask perceptions scale.

Face masks are an avenue to curb the spread of coronavirus, but few people in Western societies wear face masks. Social scientists have rarely studied face mask wearing, leaving little guidance for methods to encourage these behaviours. In the current article, we provide an approach to address this issue by developing the 32-item and 8-dimension Face Mask Perceptions Scale (FMPS). We begin by developing an over-representative item list in a qualitative study, wherein participants' responses are used to develop items to ensure content relevance. This item list is then reduced via exploratory factor analysis in a second study, and the eight dimensions of the scale are supported. We also support the validity of the FMPS, as the scale significantly relates to both face mask wearing and health perceptions. We lastly confirm the factor structure of the FMPS in a third study via confirmatory factor analysis. From these efforts, we identify an avenue that social scientists can aid in preventing coronavirus and illness more broadly - by studying face mask perceptions and behaviours.