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Bakgrund: Det anestesiologiska omvårdnadsansvaret kräver särskild kompetens och erfarenhet. Vid anestesiologisk vård såsom induktionen krävs nära samarbete. Kommunikationen i teamet har visat sig avgörande för teamsamarbetet. Faktorer som kan påverka kommunikationen är hierarki, stress, relationen till kollegor eller tron på den egna förmågan. Syfte: Att kartlägga anestesisjuksköterskors erfarenheter av kommunikationen i teamet vid induktion. Metod: Enkätstudie med kvantitativ ansats. Enkäten bestod av 35 frågor varav de första fem kartlade demografiska data. Resterande frågor bestod av påståenden vilka besvarades med likertskala. Data analyserades i Statistical Package for the Social Sciences (SPSS). Deskriptiv statistik gav en överblick av datainsamlingen och analytisk statistik användes för jämförelse av grupper. Resultat: 53 anestesisjuksköterskor besvarade enkäten. Majoriteten ansåg att kommunikationen har stor betydelse för patientsäkerheten. Samtliga uppgav att de i varierande grad vågar ifrågasätta oklarheter vid induktion. Det fanns positiva erfarenheter av att kommunikationsverktyg främjar kommunikationen. Slutsats: För en säker vård vid induktion krävs en fungerande teamkommunikation. Kommunikationen påverkas av personliga egenskaper, arbetserfarenheter, stress eller hur olika kommunikationsverktyg används. Främjande faktorer för kommunikationen är utbildning och relationsbyggnad inom teamet. Arbetserfarenhet och ålder kan påverka hur kommunikationen i teamet vid induktion uppfattas.

Background Delirium is a clinical condition characterized by an acute change in brain function and is frequently observed in critically ill patients. The condition has been associated with negative outcomes, making it crucial to identify patients who are at risk. Two recent prediction models have been developed to estimate the risk of delirium in intensive care unit (ICU) patients; the prediction model for delirium (PRE-DELIRIC) and the early prediction model for delirium (E-PRE-DELIRIC). We aimed to perform an external validation of these models in a Danish cohort of critically ill patients. Methods We conducted a prospective, observational multicenter study to validate the PRE-DELIRIC and E-PRE-DELIRIC models in a population of patients admitted to four general ICUs in the Zealand Region of Denmark. From January 2022 to January 2023 all adult patients acutely admitted to the participating ICUs were assessed for eligibility. Patients had to be admitted to the ICU for >24 h to be included in the study. Included patients were screened with E-PRE-DELIRIC upon ICU admission and PRE-DELIRIC after 24 h of admission and followed throughout their ICU stay with CAM-ICU delirium assessments. Our primary outcomes were the prognostic accuracy measured by Area Under the Receiver Operating Characteristics (AUROC) and the calibration plot for the E-PRE-DELIRIC and PRE-DELIRIC prediction models. Results We included 660 patients, of whom 660 were assessed with E-PRE-DELIRIC, and 622 were assessed with PRE-DELIRIC. PRE-DELIRIC showed acceptable discrimination with AUROC of 0.70 (95% CI 0.66 to 0.74) and good calibration. E-PRE-DELIRIC had inadequate discrimination AUROC of 0.63 (95% CI 0.58 to 0.67) and poor calibration. Conclusion In a Danish cohort, we found that the PRE-DELIRIC model demonstrated acceptable performance and E-PRE-DELIRIC demonstrated poor performance. In critically ill adult patients PRE-DELIRIC may be useful in identi, Background: Delirium is a clinical condition characterized by an acute change in brain function and is frequently observed in critically ill patients. The condition has been associated with negative outcomes, making it crucial to identify patients who are at risk. Two recent prediction models have been developed to estimate the risk of delirium in intensive care unit (ICU) patients; the prediction model for delirium (PRE-DELIRIC) and the early prediction model for delirium (E-PRE-DELIRIC). We aimed to perform an external validation of these models in a Danish cohort of critically ill patients. Methods: We conducted a prospective, observational multicenter study to validate the PRE-DELIRIC and E-PRE-DELIRIC models in a population of patients admitted to four general ICUs in the Zealand Region of Denmark. From January 2022 to January 2023 all adult patients acutely admitted to the participating ICUs were assessed for eligibility. Patients had to be admitted to the ICU for >24 h to be included in the study. Included patients were screened with E-PRE-DELIRIC upon ICU admission and PRE-DELIRIC after 24 h of admission and followed throughout their ICU stay with CAM-ICU delirium assessments. Our primary outcomes were the prognostic accuracy measured by Area Under the Receiver Operating Characteristics (AUROC) and the calibration plot for the E-PRE-DELIRIC and PRE-DELIRIC prediction models. Results: We included 660 patients, of whom 660 were assessed with E-PRE-DELIRIC, and 622 were assessed with PRE-DELIRIC. PRE-DELIRIC showed acceptable discrimination with AUROC of 0.70 (95% CI 0.66 to 0.74) and good calibration. E-PRE-DELIRIC had inadequate discrimination AUROC of 0.63 (95% CI 0.58 to 0.67) and poor calibration. Conclusion: In a Danish cohort, we found that the PRE-DELIRIC model demonstrated acceptable performance and E-PRE-DELIRIC demonstrated poor performance. In critically ill adult patients PRE-DELIRIC may be useful in identifying patients at high risk of delir

Generative machine learning models provide opportunities to support design work in various parts of the design process. This study investigates how generative machine learning and large language models may play a part in creative design processes of ideation, early prototyping and sketching. A workshop was conducted in which design practitioners and design researchers developed design concepts for a provided design case, with the help of GPT-3. The fndings point to three main themes, including i) the practical usefulness and limitations of the system in design ideation processes, ii) how the form of user interaction shapes users’ expectations of the system’s capabilities and potentials, and iii), how the broader discourse around AI both limits and enables how co-creative processes involving human and AI unfolds. The discussion outlines design implications and alternative framings of this kind of co-creative design practices based on post-human perspectives on design and technology use.

Waste management in urban areas is a complex process, encompassing a variety of activities (e.g., acquiring, sorting, disposing),actors (e.g., single individuals, waste collectors, condominium associations), and capacities (e.g., from household recycling stations to physical infrastructures such as recycling and sorting facilities).Whereas previous HCI design research has tackled problems with waste management from an individual, behavioral change perspective, we approach this design space through a feminist ecological design perspective of Digital Environmental Stewardship. Through a combination of qualitative empirical data and materials generated at design workshops, we outline challenges related to waste management in a complex of five multi-apartment buildings. We propose a number of design explorations addressing such challenges, and reflect on the generative role of the DES framework in framing design from a collective and ecological perspective.