Anna Wang

An algebraic method for information fusion based on nonadditive set functions is used to assess the joint contribution of Boolean network attributes to the sensitivity of the network to individual node mutations. The node attributes or characteristics under consideration are: in-degree, out-degree, minimum and average path lengths, bias, average sensitivity of Boolean functions, and canalizing degrees. The impact of node mutations is assessed using as target measure the average Hamming distance between a non-mutated/wild-type network and a mutated network.

IntroductionThe 2019–2020 pandemic COVID-19 has become a global health crisis. While many recent studies on COVID-19 pandemic have focused on disease epidemiology and psychological status of patients, few have explored the multi-facet influential factors or combined perspectives from both the patients and healthcare workers. The purposes of this study were to: analyze the influencing factors of psychological distress of COVID-19 patients; and describe the experience of healthcare workers relieving psychological distress.Materials and MethodsThis study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting two populations: COVID-19 patient and health workers, respectively. In the quantitative part, we recruited a convenience sample of patients with COVID-19 from five hospitals in Wuhan, Hubei Province from 10 to 15 April, 2020. Besides, we collected data by using participants’ socio-demographic information sheet, the Connor-Davidson Resilience Scale-10, the Herth Hope Index, the Distress Thermometer, the Revised Chinese Version of Mishel Uncertainty in Illness Scale, and the Chinese Version of Wake Forest Physician Trust Scale. In the qualitative part, the participants were healthcare workers involved in medical aid missions in Hubei Province, China. Meanwhile, we used sampling with convenient and purposive, data collection with a semi-structured online video interview, and text transcription with Colaizzi’s phenomenological method.ResultsThe results reveal that 25.7% of patients reported higher level of psychological distress. After controlling the sociodemographic variables, only severity of COVID-19 and uncertainty in illness shown significant effect on psychological distress in the regression model. The experience of healthcare workers emerged five themes: Particularly needed psychological care, Manifestation of negative emotion, Manifestation of proactive adaptation, Strategies relieving psychological distress, and gains of healthcare workers after delivering effective psychological care.ConclusionThe 25.7% of patients with COVID-19 still suffered from psychological distress, which should receive timely attention from healthcare workers. And the severity of the disease and disease uncertainty have a significant impact on distress. It is critical to train the healthcare workers on detecting the different manifestation of psychological distress, offering timely disease related information, and applying communication strategies.

The Mental Health Literacy Scale is the most widely used and strong theory-based measurement tool to gain an understanding of mental health knowledge and ability. This study aimed to test the psychometric properties of the Chinese version of the Mental Health Literacy Scale and to document the norm and its influential factors of mental health literacy among nurses. The MHLS was translated following Brislin’s translation model and tested with a sample of 872 clinical registered nurses. The Jefferson Scale of Empathy-Health Professionals, Patient Health Questionnaire-2, and Generalized Anxiety Disorder-2 were administered to assess convergent validity. The minimum average partial test, parallel analysis and confirmatory factor analysis supported 4 first-order 2 second-order structure. The 4 factors were named “knowledge of mental disorder,” “ability to seek information and help,” “recognition of mental disorder,” and “acceptance of patients with mental illness,” with factor 1-3 were summarized into MHLS-Core and factor 4 as MHLS-SA. The MHLS-C was moderately negatively correlated with the PHQ-2 and GAD-2 and highly positively correlated with JSE-HP. The Cronbach’s α was 0.85 for the overall scale and 0.89 and 0.93 for two subscales. The test-retest reliability was good, with intraclass correlation coefficients of 0.80 for the whole scale, and 0.79 and 0.94 for two subscales. As an approximately normal distribution, the 50th percentile for the MHLS-C was 99, with 50th percentiles of 74 and 20 for MHLS-Core and MHLS-SA. Higher position, higher professional credentials, higher hospital hierarchy, other specialist hospital, psychiatric hospital and unmarried status were positive predictors. The 29-item MHLS-C, with two subscales of MHLS-Core and MHLE-SA, is a stable and validated tool to measure mental health literacy. MHLS-Core could be used independently to measure the core content of mental health literacy. It may be applicable for Chinese health professionals, but need further validation among the general public. MHL curriculum and a targeted culturally appropriate program for acceptance for health professionals, especially for those in general hospitals and with less working tenure, may be recommended.