The present article focused on the psychometric evaluation of the Polish version of the Expressions of Spirituality Inventory-Revised (ESI-R), a measure of a five-dimensional model of spirituality that has been studied in multiple cultures. Using a large Polish adult sample (N = 2,002), we examined the reliability and factorial, criterion, and convergent validity of the test. Results provide evidence of satisfactory item- and scale-level reliability for all five ESI-R dimensions. Strong support for the criterion validity and factorial validity of the instrument was also found. Convergent validity as manifested in correlations with conceptually similar measures was supported for four of the five ESI-R dimensions. While there were a couple of points of divergence, findings from the present investigation largely replicate and extend upon results reported in other cultural contexts and provide evidence supporting the possibility that spirituality research may be generalizable across cultures. The paper ends with a discussion of meaning and implications of the findings for theory development, testing, and future research.
Plumes of calcite (limestone) in the air can be created to cool cities. The radiant properties of calcite are ideal for reflecting solar radiation while allowing ground radiation to pass through. Las Vegas is a suitable location since local limestone is available, the region experiences much sunshine daily, and air conditioning is extensively used. Calcite would be dispersed into the air from a ring of towers around the city. Modeling shows that the aerosol would block half the solar radiation and reduce maximum daily temperatures by 5 to 6 °C. A proposed system would save 38
Purpose/Objectives:The purpose of this paper is to show how a critical care clinical nurse specialist (CNS) with prescriptive authority can address immediate patient needs, drive quality improvement, and improve population health outcomes.Description:A review of the current legislative landscape suggests that CNS practice could be enhanced by adding prescriptive authority for the purposes of improving patient, nurse, and system outcomes. Though 41 states support CNS prescribing, many factors may hinder its implementation.Outcomes:By examining the values of a CNS authorized to prescribe in their healthcare organization with real-time reflections (scenarios), the ability to enhance direct patient care, reduce delays, and foster nurse engagement is revealed.Conclusion:CNSs who elect to obtain prescriptive privileges may have opportunities to improve patient services and outcomes.
The field of learning analytics has made notable strides in automating the detection of complex learning processes in multimodal data. However, most advancements have focused on individualized problem-solving instead of collaborative, open-ended problem-solving, which may offer both affordances (richer data) and challenges (low cohesion) to behavioral prediction. Here, we extend predictive models to automatically detect socially shared regulation of learning (SSRL) behaviors in collaborative computational modeling environments using embedding-based approaches. We leverage large language models (LLMs) as summarization tools to generate task-aware representations of student dialogue aligned with system logs. These summaries, combined with text-only embeddings, context-enriched embeddings, and log-derived features, were used to train predictive models. Results show that text-only embeddings often achieve stronger performance in detecting SSRL behaviors related to enactment or group dynamics (e.g., off-task behavior or requesting assistance). In contrast, contextual and multimodal features provide complementary benefits for constructs such as planning and reflection. Overall, our findings highlight the promise of embedding-based models for extending learning analytics by enabling scalable detection of SSRL behaviors, ultimately supporting real-time feedback and adaptive scaffolding in collaborative learning environments that teachers value.
