Dilemmas and Repercussions of Workplace Violence against Emergency Nurses: A Qualitative Study.

Nurses received the highest rate of workplace violence due to their close interaction with clients and the nature of their work. There have been relatively few qualitative studies focus on nurses' perceptions of and experiences with the antecedents, dilemma and repercussions of the patient and visitor violence (PVV), leaving a considerable evidence gap. The aim of this study was to explore nurses' experience of PVV in emergency department, the impact of PVV on quality of care, and supports needed after exposure to such incidents. We conducted semi-structured interviews with a purposive and snowball sample of nurses, and analyzed the content of the interview transcripts. A total of 10 nurses were approached and agreed to participate. Those participants ranged in age from 24 to 41 years old, eight female and two male nurses, and the majority of them (80%) held a university Bachelor degree in nursing. The average time in nursing practice was 7.2 years. We conceptualized five analytical themes, which comprised: (1) multifaceted triggers and causes of PVV; (2) experiences following PVV; (3) tangled up in thoughts and struggle with the professional role; (4) self-reflexivity and adjustment; and, (5) needs of organizational efforts and support following PVV. This paper provides compelling reasons to look beyond solely evaluating the existence of workplace, and considering the perceived professional inefficacy, impacts of being threatened or assaulted in nurses. There are also urgent needs in provision of prevention and management of workplace training programs to ensure the high-quality nursing care.

Effects of Integrated Workplace Violence Management Intervention on Occupational Coping Self-Efficacy, Goal Commitment, Attitudes, and Confidence in Emergency Department Nurses: A Cluster-Randomized Controlled Trial.

Patient and visitor violence (PVV), the most prevalent source of workplace violence, is largely ignored, underreported, and a persistent problem in emergency departments. It is associated with physical injuries, psychological distress, and occupational stress in nurses. A randomized controlled trial was conducted in Taiwan from January to December 2020. This study aimed to test the efficacy of an integrated Workplace Violence Prevention and Management Training Program on PVV in 75 emergency department (ED) nurses from a hospital. Cluster sampling was used because the policy of subdivision strategy was enforced during the COVID-19 pandemic. ED nurses received either the intervention or 1-hour in-service class. Data were collected from questionnaires. Data were analyzed mainly by the repeated measure analysis of variance and generalized estimating equations. The intervention had positive effects on developing stronger goal commitment, improving occupational coping self-efficacy, increasing confidence in ability to deal with violent situations, and modifying attitudes toward the causes and management of PVV in ED nurses (p < 0.05). The marginal R2 of the generalized estimating equation model for goal commitment, occupational coping self-efficacy, confidence, attitudes toward aggression in ED and aggressive behavior variables was high as 0.54 (p < 0.001), 0.45 (p < 0.001), 0.58 (p < 0.001), 0.29 (p < 0.05), and 0.72 (p < 0.001), respectively. These study models could effectively predict changes in the mean values. The benefit was driven by the effect of the intervention in ED nurses. Thus, the intervention, when applied in conjunction with routine in-service class, could exert synergistic improvements on outcomes measured in nurses.

NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis.

During pandemics (e.g., COVID-19) physicians have to focus on diagnosing and treating patients, which often results in that only a limited amount of labeled CT images is available. Although recent semi-supervised learning algorithms may alleviate the problem of annotation scarcity, limited real-world CT images still cause those algorithms producing inaccurate detection results, especially in real-world COVID-19 cases. Existing models often cannot detect the small infected regions in COVID-19 CT images, such a challenge implicitly causes that many patients with minor symptoms are misdiagnosed and develop more severe symptoms, causing a higher mortality. In this paper, we propose a new method to address this challenge. Not only can we detect severe cases, but also detect minor symptoms using real-world COVID-19 CT images in which the source domain only includes limited labeled CT images but the target domain has a lot of unlabeled CT images. Specifically, we adopt Network-in-Network and Instance Normalization to build a new module (we term it NI module) and extract discriminative representations from CT images from both source and target domains. A domain classifier is utilized to implement infected region adaptation from source domain to target domain in an Adversarial Learning manner, and learns domain-invariant region proposal network (RPN) in the Faster R-CNN model. We call our model NIA-Network (Network-in-Network, Instance Normalization and Adversarial Learning), and conduct extensive experiments on two COVID-19 datasets to validate our approach. The experimental results show that our model can effectively detect infected regions with different sizes and achieve the highest diagnostic accuracy compared with existing SOTA methods.