A case for the use of deep learning algorithms for individual and population level assessments of mental health disorders: Predicting depression among China's elderly

Background: With the continuous advancement of age in China, attention should be paid to the mental well-being of the elderly population. The present study uses a novel machine learning (ML) method on a large representative elderly database in China as a sample to predict the risk factors of depression in the elderly population from both holistic and individual level. Methods: A total of participants met the inclusion criteria from the fourth waves of the China Health and Retirement Longitudinal Study (CHARLS) were analyzed with ML algorithms. The level of depression was assessed by the 10-item Center for Epidemiological Studies Depression Scale (CESD-10). Results: The current study found top 5 factors that were important for predicting depression in the elderly population in China, including average sleep time, gender, age, social activities and nap time during the day. The results also provide reliable diagnostic likelihood at the individual level to support clinicians identify the most impactful factors contributing to patient depression. Our findings also suggested that activities such as interacting with friends and play ma-Jong, chess or join community clubs may have a positive collaborative effect for elderly's mental health. Conclusions: Holistic approaches are an effective method of deriving and interpreting sophisticated models of mental health in elderly populations. More detailed information about a patient's demographics, medical history, sleeping patterns and social/leisure activities can help to inform policy and treatment interventions on a population and individual level. Large scale surveys such as CHARLS are effective methods for testing the most accurate models, however, further research using professional clinical input could further advance the field

Nursing students' knowledge of working with D/deaf and hard of hearing patients: Evaluation of a deaf awareness elearning package

AimThe aim of this study was to evaluate a newly developed Deaf awareness e-learning package with nursing students at one university in Wales, UK.BackgroundD/deaf and hard of hearing communities face a multitude of barriers when accessing and receiving healthcare leading to under diagnosis of health conditions and poorer health outcomes in general. Lack of awareness, teaching, and exposure to the D/deaf and hard of hearing populations during health care professional training programmes has been shown to contribute to this health disparity.DesignA descriptive cross-sectional design was used with two cohorts of undergraduate nursing students at one university in Wales, UK who were invited to undertake a Deaf awareness eLearning package developed with D/deaf communities in Wales.MethodsNursing student engagement and course completion were monitored, and evaluation survey questionnaires were implemented.ResultsThe Deaf awareness eLearning package evaluation showed engagement with over 400 nursing students, who scored the package an overall mark (1 to 5 stars) of 4.72 out of 5. In total, 227 nursing students completed the eLearning package and received the certificate. Students reported finding the eLearning package very interactive, easy to navigate, thought the three-hour length was about right. However, we would like to know more about factors that influence student non-engagement and dropout.ConclusionsThese findings suggest that eLearning Deaf awareness programs can be successful in increasing knowledge and confidence around communicating with D/deaf and hard of hearing patients for nursing, with potential benefits for wider rollout across wider health and care student and staff populations

Connectedness and frequency connection among green bond, cryptocurrency and green energy-related metals around the COVID-19 outbreak

We investigate the return interdependence among green bonds, cryptocurrency indices and green energy-related metals. We apply time-varying parametric vector autoregression (TVP-VAR) conenctedness, wavelet coherence, Wavelet Quantile Correlation （WQC) and Quantile on Quantile (QQR) Connectedness Methods. Our empirical findings show that return connectedness has become even stronger after the outbreak of COVID-19, with both green bonds and cryptocurrency indices acting as net receivers of return spillovers. Surprisingly, Copper functioned as a net sender of return spillovers over the entire observation period. Findings revealed that the cryptocurrency index exhibited a consistent positive correlation with the green energy-related metals market at medium to short-term frequencies, whereas green bonds showed a negative correlation with metals market at short-term frequencies and a positive correlation at long-term frequencies

Using Historical Approaches to Understand Contemporary Student Loneliness

Student loneliness is a global problem, with universities struggling to tackle an issue that has important implications for student success, satisfaction, and mental health. This research uses archival material from the 1960s and 1970s alongside qualitative discussions with contemporary students to explore the ways that experiences of loneliness within British higher education have changed across recent decades. Such an approach bridges the divergent approaches taken by different scholarly disciplines, applying focus group methodologies to the consideration of archival material. For this project five focus groups were held with undergraduate students at four universities in England, Wales and Scotland. This article argues for the contemporary relevance of historical research into student loneliness, exploring student responses to their predecessors’ experiences of loneliness. It argues that equipping undergraduates with a deeper knowledge about their forerunners’ experiences of disconnection can trouble some of the stereotypes, assumptions, and expectations around the sociable ‘student experience’ today. Such an approach has widespread implications for researchers’ and policy makers’ understandings of the potential role of interdisciplinary and humanities-generated knowledge in addressing social problems within higher education

Evaluation of the effect of a biomass fuel source on the thermal properties of iron ore sinter

The knowledge around the effect of bioenergy on the thermal properties of iron ore sinter is not widely understood. Therefore, the effects of a 30 % biomass hybrid was investigated. Experiments placed samples in thermal environments encapsulating radiant, convective and conductive heating at increasing thermal gradients. Temperature data was collected using a longwave IR thermal camera, prompting a gap in literature knowledge – ”Does emissivity vary as sinter undergoes thermal change?” to be studied. Furnace data in the range of 200 °C–600 °C showed an increasing trend in emissivity from 0.82 to 0.93 with a deviation of <2 % between 0 and 100 % hybrid samples. The results of the subsequent thermal tests indicated an initial barrier to energy absorption caused by the morphology of the sinter that decreased with the thermal gradient. Statistical analysis concluded that the 75 % blend, absorbed energy at a consistently high rate in all the heating environments. Linear regression analysis with x-ray fluorescence and diffraction data showed that the quantity of FeO, prismatic SFCA and platy SFCA had a measurable effect on the heating rate at 400 °C. However, as temperatures increased to 600 °C Fe2O3 had more effect than FeO, with the SFCA phases maintaining their impact on heating rate