Introduction This study aimed to assess the psychometric validity of the 12-item Burnout Assessment Tool (BAT-12) among bus drivers - a high-risk but understudied occupational group - across the three main linguistic regions of Switzerland (German, French, and Italian). Methods The study sample comprised 494 bus drivers who completed the BAT-12 in the Transport Personnel Health Cohort (TRAPHEAC) study. Content validity was assessed by expert ratings and user interviews in French. Structural validity was examined using CFA, and cross-cultural validity through differential item functioning (DIF) analyses. Construct validity was evaluated through convergent, divergent, and discriminative analyses based on pre-defined hypotheses. Results Both the correlated four-factor and second-order four-factor CFA models showed satisfactory fit, supporting the multidimensional structure of the BAT-12. For the French version, 9 of the 12 items met the prespecified item-level content validity threshold, while the scale-level S-CVI/Ave was 0.861, approaching but not attaining the 0.90 value recommended for excellent content validity. Ordinal logistic regression identified uniform DIF for items 2 and 7 in the German-French comparison and for items 2 and 12 in the German–Italian comparison; non-uniform DIF was detected for item 8 in the German-Italian comparison. Sensitivity analyses indicated that excluding DIF-flagged items had little impact on the overall between-language comparisons. Most construct-validity hypotheses were supported, although the association between the BAT-12 and the BDI-II was stronger than expected. Discriminative validity analyses showed higher BAT-12 scores among groups expected to be at greater risk of burnout. Conclusion In Swiss bus drivers, the BAT-12 showed good structural and construct validity. Cross-language content comparability requires further investigation.
Electroencephalography is the most commonly used diagnostic tool for epilepsy. However, interpreting electroencephalograms (EEGs) requires expertise that is not widely available. Advances in digital technology and wearables have enabled large-scale EEG recording, generating vast amounts of data that cannot be managed through traditional visual interpretation by experts. Artificial intelligence (AI) has the potential to augment human expertise and reduce workloads. The application of artificial neural networks in analysing clinical EEG recordings has led to major breakthroughs, bringing AI-based EEG interpretation closer to clinical implementation. In this Review, we summarise the most important research and development results in this field from a clinical perspective. We provide an overview of AI applications in spike and seizure detection; analysis of data from wearable electroencephalographs, patients who are critically ill, and epilepsy surgery; and the automated interpretation of clinical EEGs.
Exposure to environmental stressors such as hypoxia or heat has emerged as an effective strategy to stimulate physiological adaptations, enhance sport/physical performance and promote health. Both environmental stressors activate shared molecular pathways, notably through the stabilisation of hypoxia-inducible factor-1 alpha and the induction of heat shock proteins, which mediate cellular protection and systemic molecular adaptation. However, hypoxia and heat differ in several aspects, including induction modalities, monitoring methods, metrics used to assess physiological strain and substantial intra- and inter-individual variability. Together, these differences challenge direct comparison and/or combination. Therefore, this current opinion highlights research gaps by critically presenting the common and distinct physiological responses and adaptations to hypoxia and heat exposure. It also emphasises the importance of monitoring internal physiological strain rather than external environmental stress to better account for individual variability. Finally, we propose future research perspectives to address current methodological challenges.
Mount Etna, one of the most active volcanoes on Earth, exhibits unusual characteristics in terms of its geological setting, effusion rate, and volatile content. Mount Etna is closely related to the subduction of the Ionian plate below Eurasia, but, chemically, Mount Etna lavas are akin to intraplate magmatism. Unlike typical oceanic islands such as Hawaii, Mount Etna eruptive sequence starts with small volumes of tholeiitic lava, then shifts to alkaline compositions with much higher effusion rates, raising questions about its magma sources and modes of generation. Here, we propose that the lavas erupted at Mount Etna originate from the extraction of pre-existing melts stored within the Low Velocity Zone (LVZ). This zone characterizes the lithosphere-asthenosphere boundary (LAB) and is identified beneath oceanic plates by electric and seismic anomalies interpreted to reflect the presence of small melt fractions. We use geochemical data and modeling to show that extraction of such pre-existing melts in the LVZ in response to plate flexure around Sicily and, when coupled with varying degrees of melt-rock interaction, accounts for the temporal evolution of magma chemistry and eruptive volume at both Mount Etna and the earlier-formed Hyblean Plateau. Our analysis suggests that extraction of low degree melts from the LVZ can give rise to large alkaline volcanoes in specific tectonic environments. Mount Etna may be a unique place on Earth where the compositions of melts at the LAB can be studied directly on the Earth's surface.
The relationship between self-reported falls and fracture risk was estimated in an international meta-analysis of individual-level data from 46 prospective cohorts. Previous falls were associated with an increased fracture risk in women and men and should be considered as an additional risk factor in the FRAX® algorithm. Previous falls are a well-documented risk factor for subsequent fracture but have not yet been incorporated into the FRAX algorithm. The aim of this study was to evaluate, in an international meta-analysis, the association between previous falls and subsequent fracture risk and its relation to sex, age, duration of follow-up, and bone mineral density (BMD). The resource comprised 906,359 women and men (66.9
