Regional Health Administration of Thessaly

Healthcare professionals experience high levels of burnout and chronic stress, which further jeopardizes their optimism and mental resilience. The study involved 175 health professionals (46 doctors, 56 nurses, 27 nursing assistants, 23 ward assistants, 23 paramedical workers) of the Sismanogleio General Hospital of Athens, aged 26-65. Participants were administered an electronic questionnaire, which included socio-demographic data and work-related information, the Revised Life Orientation Test (LOT-R) and the Connor-Davidson Resilience Scale (CD-RISK-25). The data analysis was conducted using SPSS, with Descriptive and Inferential Statistics methods. The results reveal moderate levels of mental resilience (Mean=65.23, Std. Dev.=15.44) and moderate levels of optimism (Mean=12.75, Std. Dev.=2.89) among participants. Health professionals' levels of optimism are positively correlated with their levels of mental resilience (rho = 0.208, p = 0.006). The levels of optimism and mental resilience do not differ according to gender, marital status, or years of work experience (p = 0.612, p = 0.458, p = 0.214, respectively), but differ according to age (p < 0.001). Health professionals aged 60+ show the highest levels of mental resilience, while paramedical workers demonstrate greater optimism compared to physicians. The development of targeted training and support programs aimed at strengthening optimism and mental resilience among healthcare professionals may be beneficial.

This research aims to analyze discrimination experienced by individuals with di verse gender identities and sexual orientations in their daily lives when seeking medical assistance, and its impact on their quality of life. Conducted as a cross-sectional study, it utilized a questionnaire developed based on the 2019 Southern LGBTQ Health Survey. Linear regression analysis identified factors influencing difficulties accessing health and public services, and Quality of Life SF-12 scores. The sample comprised 152 individuals, predominantly aged 18-30, female, and identifying as bisexual. Most resided in urban areas and were educated, yet a significant part of them were unemployed. While many preferred private clinics for medical care, few delayed seeking care due to LGBTQ+ identity. Participants reported workplace difficulties and experienced violence, correlating with poorer physical and mental health. Positive opinions about healthcare services corresponded to better mental health. Educational level and employment status influenced mental well-being. Exclusion from healthcare and public services detrimentally affected LGBTQ+ individuals' mental health, emphasizing the need for inclusive policies, education for healthcare professionals, and specialized services. Efforts to promote inclusion across social groups are imperative for enhancing overall well-being and ensuring equitable access to healthcare.

Edge artificial intelligence (EDGE-AI) refers to the execution of artificial intelligence algorithms on hardware devices while processing sensor data/signals in order to extract information and identify patterns, without utilizing the cloud. In the field of predictive maintenance for industrial applications, EDGE-AI systems can provide operational state recognition for machines and production chains, almost in real time. This work presents two methodological approaches for the detection of the operational states of a DC motor, based on sound data. Initially, features were extracted using an audio dataset. Two different Convolutional Neural Network (CNN) models were trained for the particular classification problem. These two models are subject to post-training quantization and an appropriate conversion/compression in order to be deployed to microcontroller units (MCUs) through utilizing appropriate software tools. A real-time validation experiment was conducted, including the simulation of a custom stress test environment, to check the deployed models’ performance on the recognition of the engine’s operational states and the response time for the transition between the engine’s states. Finally, the two implementations were compared in terms of classification accuracy, latency, and resource utilization, leading to promising results.

Abstract Background The unprecedented increase in ageing population in recent decades has led to a shift in the epidemiological profile of societies, resulting in an increased prevalence of chronic diseases, cognitive impairments, and sensory loss in older adults and their elevated reliance on both formal and informal caregiving. Caregivers hold a vital role in older adults’ healthcare, yet their health and well-being needs are often undermined. This paper ventures the exploration of both formal and informal caregivers’ profiles through the development of personas. Methods Formal caregivers (N = 71) and informal caregivers (N = 54) were recruited in the study participating both in quantitative surveys and ad-hoc interviews including the measures Job Stress Scale, Zarit Burden Interview, Short Form Health Survey (SF-36) and Persona Perception Scale. Results Upon the analysis of the Persona Perception Scale and Job Stress Scale, the findings revealed high reliability scores, designating that the formal caregiver persona ‘’Daphne’’ could adequately represent the actual target group. The results also showed that the informal caregiver persona ‘’Elle’’ was an accurate reflection of the corresponding target group, after analyzing the findings retrieved from quality of life measurements and caregivers’ burden. Conclusions User personas can enhance a deeper understanding of the target group and apprehend user preferences and experiences. To this end, personas can potentially provide empathetic and expansive benefits and be exploited as artifacts in attempting to successfully liaise with policy-makers and care providers in aligning key strategies and policies that will conclusively improve the quality of life of both care recipients and caregivers.

The purpose of this study is to investigate the relationship between continuously monitored regional cerebral blood flow (CBF) and microdialysis values in severe subarachnoid hemorrhage and traumatic brain injury patients.