Haute école de santé Genève

Objectives: To identify subgroups of patients with distinct psychological profiles at the beginning of the COVID-19 pandemic and evaluate for differences. Sample & setting: Online survey of patients with cancer during the COVID-19 pandemic. Methods & variables: Patients completed measures of demographic and clinical characteristics, as well as cancer- and COVID-19-related stress, global stress, social isolation, loneliness, financial toxicity, and common symptoms. Latent profile analysis was used to identify distinct psychological profiles. Results: Among 1,145 patients, three subgroups were identified (i.e., no anxiety or depression and normative level of resilience; high depression, high anxiety, and low resilience; and very high depression, very high anxiety, and very low resilience). Patients with the two worst psychological profiles were younger, more likely to be female, more recently diagnosed with cancer, and more likely to have breast cancer. Implications for nursing: Findings may assist clinicians to identify patients at increased risk for significant psychological morbidity and provide more timely, targeted, and cost-effective interventions.

Supercontinuum generation (SCG) from silica‐based photonic crystal fibers (PCFs) is of highly technological significance from microscopy to metrology, but has been hindered by silica's relatively low intrinsic optical nonlinearity. The prevailing approaches of filling PCF with nonlinear gases or liquids can endow fibre with enhanced optical nonlinearity and boosted SCG efficiency, yet these hybrids are easily plagued by fusion complexity, environmental incompatibility or transmission mode instability. Here this work presents a strategy of embedding solid‐state 2D MoS2 atomic layers into the air‐holes of PCF to efficiently enhance SCG. This work demonstrates a 4.8 times enhancement of the nonlinear coefficient and a 70% reduction of the threshold power for SCG with one octave spanning in the MoS2‐PCF hybrid. Furthermore, this work finds that the SCG enhancement is highly layer‐dependent, which only manifests for a real 2D regime within the thickness of five atomic layers. Theoretical calculations reveal that the critical thickness arises from the trade‐off among the layer‐dependent enhancement of the nonlinear coefficient, leakage of fundamental mode and redshift of zero‐dispersion wavelength. This work provides significant advances toward efficient SCG, and highlights the importance of matching an appropriate atomic layer number in the design of functional 2D material optical fibers.

The purpose of this article is to introduce a design-based research (DBR) approach developed in the field of vocational and continuing education, which is grounded in a pragmatic and phenomenologically inspired enactivist approach to activity. As a design-based methodology, our activity-centered and enactive DBR approach aims to generate knowledge related to design and to identify relevant design principles. After detailing the particularities of an activity-centered and enactive DBR approach, we focus on the results pertaining to design knowledge by identifying two broad design principles for vocational education and training, and five enactivist inspired principles for training design. A significant practical implication for researchers and practitioners in vocational and continuing education and training is that these enactivist inspired design principles provide promising pathways to enhance the connectivity between (i) work experiences, (ii) work and training practices, and (iii) learning contexts.

We investigated the influence of family alliance on infants’ vagal tone. Physiological studies have shown that the quality of mother–infant interactions can influence infants’ vagal tone, which is an important indicator of emotion regulation. Although research has shown that family‐level relationships have a unique impact on child development, little is known about the association between the quality of mother–father–infant interactions and infants’ physiological regulation during a family interaction task. We hypothesized that infants in families with a greater family alliance, that is, degree of coordination reached by parents when completing a task, will have higher vagal tone than will infants in families with a lower family alliance. We also hypothesized that this association would be mediated by the amount of intrusive and withdrawn parental behaviors and by the impact of these behaviors on the infant during mother–father–infant interactions. This study included 82 parents with their 3‐month‐old infants. Results showed that family alliance was associated with infants’ vagal tone during triadic interactions and that the impact of intrusive/withdrawn parental behaviors on the interaction partially mediated this association.

Cancer is the leading cause of death worldwide, accounting for about one among six deaths, so the quest for new and improved therapies is of crucial importance. The discovery of...

Introduction More than 50% of Swiss adult patients with hypertension do not reach blood pressure targets.1 2 Team-based care can be an effective and safe alternative to clinic-based care for improving blood pressure control and patient-centered care for hypertension.³ As we move toward a more digitalized and connected healthcare system, patients are becoming empowered, with the ability to share self-generated data and participate in decision-making.⁴ The aim of this study is to explore patients’, pharmacists’, and general practitioners’ (GPs’) attitudes and preferences towards a digital platform that coordinates hypertension management within an interprofessional care model facilitating detailed exchange of information between these stakeholders. Methods This exploratory cross-sectional study aims to include 215–250 Swiss adults ≥18 years old who are diagnosed with high blood pressure (self-report), pharmacists and GPs practicing in Swiss primary care settings. Online anonymous surveys are distributed in three national languages (German, French and Italian) to each participant group. Recruitment of healthcare professionals is facilitated by professional associations. Patients are mainly recruited through healthcare providers. The surveys contain questions about information sources and currently used tools for hypertension management. Additionally, they explore potential functionalities for a digital interprofessional platform dedicated to hypertension management. The data will be analyzed using descriptive statistics. Results Recruitment for the study is ongoing. Results to be presented at the conference include a comparative analysis of attitudes and preferences towards an interprofessional digital platform for the management of hypertension among hypertensive patients, pharmacists, and GPs. Conclusion(s) This study is the first step to ensure that design and features of a digital hypertension management platform align closely with the preferences, needs, and experiences of stakeholders involved in hypertension management in Swiss primary care settings. The results will highlight features to prioritize for an increased patient involvement and interprofessional collaboration in hypertension management. References • Giezendanner S, et al, Effect of guideline revisions by the swiss society of hypertension on blood pressure control in hypertensive patients from primary care. Swiss Med Wkly, 2020; 150 :20279. • Damianaki A, et al. High blood pressure screening in pharmacies during may measurement month campaigns in switzerland. Blood Press, 2022; 31 (1):129–138. • Margolis KL, et al. Comparing pharmacist-led telehealth care and clinic-based care for uncontrolled high blood pressure: the hyperlink 3 pragmatic cluster-randomized trial. Hypertension, 2022; 79 (12):2708–2720. • El-Osta A, C Rowe, A Majeed. Developing a shared definition of self-driven healthcare to enhance the current healthcare delivery paradigm. Journal of the Royal Society of Medicine, 2022; 115 (11):424–428.

This study addresses the role of implicit attitudes toward risk in inventory management, specifically in the context of the newsvendor problem (NVP). Using the Implicit Association Test (IAT), we explore how implicit attitudes toward risk influence newsvendors' ordering decisions, focusing on deviations from profit-maximising solutions. Our methodology combines the NVP inventory exercise with IAT measurements. We find a negative correlation between participants' implicit attitudes toward risk and the absolute deviation from the optimal order quantity, indicating that individuals with implicit attitudes toward risk are closer to neutrality deviate less from the profit-maximising solution. This supports our hypothesis that implicit risk attitudes impact such deviations. Our findings emphasise the importance of considering implicit attitudes toward risk in decision support systems for inventory management. Future research directions should explore the interplay of implicit attitudes toward risk with cognitive factors and its applicability in diverse contexts.

This study examines the influence of meteorological factors and air pollutants on the performance of automatic pollen monitoring devices, as part of the EUMETNET Autopollen COST ADOPT-intercomparison campaign held in Munich, Germany, during the 2021 pollen season. The campaign offered a unique opportunity to compare all automatic monitors available at the time, a Plair Rapid-E, a Hund-Wetzlar BAA500, an OPC Alphasense, a KH-3000 Yamatronics, three Swisens Polenos, a PollenSense APS, a FLIR IBAC2, a DMT WIBS-5, an Aerotape Sextant, to the average of four manual Hirst traps, under the same environmental conditions. The investigation aimed to elucidate how meteorological factors and air pollution impact particle capture and identification efficiency. The analysis showed coherent results for most devices regarding the correlation between environmental conditions and pollen concentrations. This reflects on one hand, a significant correlation between weather and airborne pollen concentration, and on the other hand the capability of devices to provide meaningful data under the conditions under which measurements were taken. However, correlation strength varied among devices, reflecting differences in design, algorithms, or sensors used. Additionally, it was observed that different algorithms applied to the same dataset resulted in different concentration outputs, highlighting the role of algorithm design in these systems (monitor + algorithm). Notably, no significant influence from air pollutants on the pollen concentrations was observed, suggesting that any potential difference in effect on the systems might require higher air pollution concentrations or more complex interactions. However, results from some monitors were affected to a minor degree by specific weather variables. Our findings suggest that the application of real-time devices in urban environments should focus on the associated algorithm that classifies pollen taxa. The impact of air pollution, although not to be excluded, is of secondary concern as long as the pollution levels are similar to a large European city like Munich.

Tinnitus is the perception of sounds like ringing or buzzing in the ears without any external source, varying in intensity and potentially becoming chronic. This study aims to enhance the understanding and treatment of tinnitus by analyzing a dataset related to tinnitus therapy, focusing on electroencephalography (EEG) signals from patients undergoing treatment. The objectives of the study include applying various preprocessing techniques to ensure data quality, such as noise elimination and standardization of sampling rates, and extracting essential features from EEG signals, including power spectral density and statistical measures. The novelty of this research lies in its innovative approach to representing different channels of EEG signals as new graph network representations without losing any information. This transformation allows for the use of Graph Neural Networks (GNNs), specifically Graph Convolutional Networks (GCNs) combined with Long Short-Term Memory (LSTM) networks, to model intricate relationships and temporal dependencies within the EEG data. This method enables a comprehensive analysis of the complex interactions between EEG channels. The study reports an impressive accuracy rate of 99.41%, demonstrating the potential of this novel approach. By integrating graph representation and deep learning, this research introduces a new methodology for analyzing tinnitus therapy data, aiming to contribute to more effective treatment strategies for tinnitus sufferers.

Background In the initial phase of the SARS-CoV-2 pandemic, masking has been widely accepted in healthcare institutions to mitigate the risk of healthcare-associated infection. Evidence, however, is still scant and the role of masks in preventing healthcare-associated SARS-CoV-2 acquisition remains unclear.We investigated the association of variation in institutional mask policies with healthcare-associated SARS-CoV-2 infections in acute care hospitals in Switzerland during the BA.4/5 2022 wave. Methods SARS-CoV-2 infections in hospitalized patients between June 1 and September 5, 2022, were obtained from the “Hospital-based surveillance of COVID-19 in Switzerland”-database and classified as healthcare- or community-associated based on time of disease onset. Institutions provided information regarding institutional masking policies for healthcare workers and other prevention policies. The percentage of healthcare-associated SARS-CoV-2 infections was calculated per institution and per type of mask policy. The association of healthcare-associated SARS-CoV-2 infections with mask policies was tested using a negative binominal mixed-effect model. Results We included 2’980 SARS-CoV-2 infections from 13 institutions, 444 (15%) were classified as healthcare-associated. Between June 20 and June 30, 2022, six (46%) institutions switched to a more stringent mask policy. The percentage of healthcare-associated infections subsequently declined in institutions with policy switch but not in the others. In particular, the switch from situative masking (standard precautions) to general masking of HCW in contact with patients was followed by a strong reduction of healthcare-associated infections (rate ratio 0.39, 95% CI 0.30–0.49). In contrast, when compared across hospitals, the percentage of health-care associated infections was not related to mask policies. Conclusions Our findings suggest switching to a more stringent mask policy may be beneficial during increases of healthcare-associated SARS-CoV-2 infections at an institutional level.

Epilepsy is characterized by recurring seizures that result from abnormal electrical activity in the brain. These seizures manifest as various symptoms including muscle contractions and loss of consciousness. The challenging task of detecting epileptic seizures involves classifying electroencephalography (EEG) signals into ictal (seizure) and interictal (non-seizure) classes. This classification is crucial because it distinguishes between the states of seizure and seizure-free periods in patients with epilepsy. Our study presents an innovative approach for detecting seizures and neurological diseases using EEG signals by leveraging graph neural networks. This method effectively addresses EEG data processing challenges. We construct a graph representation of EEG signals by extracting features such as frequency-based, statistical-based, and Daubechies wavelet transform features. This graph representation allows for potential differentiation between seizure and non-seizure signals through visual inspection of the extracted features. To enhance seizure detection accuracy, we employ two models: one combining a graph convolutional network (GCN) with long short-term memory (LSTM) and the other combining a GCN with balanced random forest (BRF). Our experimental results reveal that both models significantly improve seizure detection accuracy, surpassing previous methods. Despite simplifying our approach by reducing channels, our research reveals a consistent performance, showing a significant advancement in neurodegenerative disease detection. Our models accurately identify seizures in EEG signals, underscoring the potential of graph neural networks. The streamlined method not only maintains effectiveness with fewer channels but also offers a visually distinguishable approach for discerning seizure classes. This research opens avenues for EEG analysis, emphasizing the impact of graph representations in advancing our understanding of neurodegenerative diseases.

Exposure to nanoparticles (NPs) in pregnancy is increasingly linked to adverse effects on embryo‐fetal development and health later in life. However, the developmental toxicity mechanisms of NPs are largely unknown, in particular potential effects on the placental secretome, which orchestrates many developmental processes pivotal for pregnancy success. This study demonstrates extensive material‐ and pregnancy stage‐specific deregulation of placental signaling from a single exposure of human placental explants to physiologically relevant concentrations of engineered (silica (SiO2) and titanium dioxide (TiO2) NPs) and environmental NPs (diesel exhaust particles, DEPs). This includes a multitude of secreted inflammatory, vascular, and endocrine placental factors as well as extracellular vesicle (EV)‐associated proteins. Moreover, conditioned media (CM) from NP‐exposed explants induce pronounced anti‐angiogenic and anti‐vasculogenic effects, while early neurodevelopmental processes are only marginally affected. These findings underscore the potential of metal oxide NPs and DEPs for widespread interference with the placental secretome and identify vascular morphogenesis as a sensitive outcome for the indirect developmental toxicity of different NPs. Overall, this work has profound implications for the future safety assessment of NPs for industrial, commercial, or medical applications in pregnancy, which should consider placenta‐mediated toxicity by holistic secretomics approaches to ensure the development of safe nanotechnologies.

Sustainable human resource management is gaining importance in organizations due to its role in developing a sustainable work environment and well-being. This paper discusses the relationship between employee perceptions of sustainable human resource management and job satisfaction in 54 countries. We propose that sustainable HRM is positively associated with job satisfaction but that this relationship is moderated by employees’ identification with the organization and country-level individualism-collectivism. Thus, we suggest national culture functions as a second-level moderator of the relationship of sustainable HRM with organizational identification on job satisfaction. Findings from the multi-level analyses using data from 14,502 employees nested within 54 countries provided support for our hypotheses, namely that employee perceptions of sustainable HRM were positively associated with job satisfaction and that this relationship was more pronounced for employees with lower levels compared to higher levels of organizational identification in individualistic rather than collectivistic countries. These findings bear important implications for both theory and practice.