Hochschule für Angewandte Psychologie FHNW
Dauerhafte URI für den Bereichhttps://irf.fhnw.ch/handle/11654/1
Listen
73 Ergebnisse
Bereich: Suchergebnisse
Publikation Dilemmakompetenz. Schwierige Entscheidungen schaffen, ohne von ihnen geschafft zu werden(edition FFAS, 2024) Küllenberg, Janna; Stößel, Ulrich; Reschauer, Georg; Michaelis, Martina04B - Beitrag KonferenzschriftPublikation Managers perception of hospital employees’ effort-reward imbalance(BioMed Central, 2023) Heming, Meike; Siegrist, Johannes; Erschens, Rebecca; Genrich, Melanie; Hander, Nicole R.; Junne, Florian; Küllenberg, Janna; Müller, Andreas; Worringer, Britta; Angerer, PeterAbstract Objective Hospitals are frequently associated with poor working conditions that can lead to work stress and increase the risk for reduced employee well-being. Managers can shape and improve working conditions and thereby, the health of their teams. Thus, as a prerequisite, managers need to be aware of their employees’ stress levels. This study had two objectives: At first, it aimed to test the criterion validity of the Effort-Reward Imbalance (ERI) questionnaire measuring psychosocial workload in hospital employees. Secondly, mean scales of the ERI questionnaire filled in by employees were compared with mean scales of an adapted ERI questionnaire, in which managers assessed working conditions of their employees. Methods Managers (n = 141) from three hospitals located in Germany assessed working conditions of their employees with an adapted external, other-oriented questionnaire. Employees (n = 197) of the mentioned hospitals completed the short version of the ERI questionnaire to assess their working conditions. Confirmatory factor analyses (CFA) were applied to test factorial validity, using the ERI scales for the two study groups. Criterion validity was assessed with multiple linear regression analysis of associations between ERI scales and well-being among employees. Results The questionnaires demonstrated acceptable psychometric properties in terms of internal consistency of scales, although some indices of model fit resulting from CFA were of borderline significance. Concerning the first objective, effort, reward, and the ratio of effort-reward imbalance were significantly associated with well-being of employees. With regard to the second objective, first tentative findings showed that managers’ ratings of their employees’ effort at work was quite accurate, whereas their reward was overestimated. Conclusions With its documented criterion validity the ERI questionnaire can be used as a screening tool of workload among hospital employees. Moreover, in the context of work-related health promotion, managers’ perceptions of their employees’ workload deserve increased attention as first findings point to some discrepancies between their perceptions and those provided by employees.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Mental health in the workplace hospital – results of the RCT “SEEGEN”(Karger, 2024) Hander, Nicole R.; Klein, Thomas; Mulfinger, Nadine; Jarczok, Marc; Rieger, Monika A.; Junne, Florian; Erschens, Rebecca; Maatouk, Imad; Küllenberg, Janna; Ruhle, Sascha; Süß, Stefan; Puschner, Bernd; Sander, Anja; Müller, Andreas; Angerer, Peter; Gündel, Harald; Rothermund, EvaAbstracts of the 27th ICPM World Congress in Tübingen September 202401A - Beitrag in wissenschaftlicher ZeitschriftPublikation Insights on the current state and future outlook of AI in health care: expert interview study(JMIR Publications, 2023) Hummelsberger, Pia; Koch, Timo K.; Rauh, Sabrina; Dorn, Julia; Lermer, Eva; Raue, Martina; Hudecek, Matthias; Schicho, Andreas; Colak, Errol; Ghassemi, Marzyeh; Gaube, SusanneBackground Artificial intelligence (AI) is often promoted as a potential solution for many challenges health care systems face worldwide. However, its implementation in clinical practice lags behind its technological development. Objective This study aims to gain insights into the current state and prospects of AI technology from the stakeholders most directly involved in its adoption in the health care sector whose perspectives have received limited attention in research to date. Methods For this purpose, the perspectives of AI researchers and health care IT professionals in North America and Western Europe were collected and compared for profession-specific and regional differences. In this preregistered, mixed methods, cross-sectional study, 23 experts were interviewed using a semistructured guide. Data from the interviews were analyzed using deductive and inductive qualitative methods for the thematic analysis along with topic modeling to identify latent topics. Results Through our thematic analysis, four major categories emerged: (1) the current state of AI systems in health care, (2) the criteria and requirements for implementing AI systems in health care, (3) the challenges in implementing AI systems in health care, and (4) the prospects of the technology. Experts discussed the capabilities and limitations of current AI systems in health care in addition to their prevalence and regional differences. Several criteria and requirements deemed necessary for the successful implementation of AI systems were identified, including the technology’s performance and security, smooth system integration and human-AI interaction, costs, stakeholder involvement, and employee training. However, regulatory, logistical, and technical issues were identified as the most critical barriers to an effective technology implementation process. In the future, our experts predicted both various threats and many opportunities related to AI technology in the health care sector. Conclusions Our work provides new insights into the current state, criteria, challenges, and outlook for implementing AI technology in health care from the perspective of AI researchers and IT professionals in North America and Western Europe. For the full potential of AI-enabled technologies to be exploited and for them to contribute to solving current health care challenges, critical implementation criteria must be met, and all groups involved in the process must work together.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Veränderungserwartungen im Kontext der Nierenlebendspende: ein Vergleich zwischen verschiedenen Spender-Empfänger-Paaren(Dustri-Verlag, 2014) Hudecek, Matthias; Schneewind, Klaus A.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays(Nature, 2023) Gaube, Susanne; Suresh, Harini; Raue, Martina; Lermer, Eva; Koch, Timo K.; Hudecek, Matthias; Ackery, Alun D.; Grover, Samir C.; Coughlin, Joseph F.; Frey, Dieter; Kitamura, Felipe C.; Ghassemi, Marzyeh; Colak, ErrolArtificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.01A - Beitrag in wissenschaftlicher ZeitschriftPublikation Schichtübergabe in der chirurgischen Intensivstation. Ein Safety-II Ansatz zur Entwicklung eines neuen Instruments zur Unterstützung von Pflegenden(Hochschule für Angewandte Psychologie FHNW, 05.09.2024) Zinsli, Patrick; Wäfler, Toni; Kantonsspital St. GallenSchichtwechsel von Pflegenden zu Pflegenden stellen besonders in Intensivstationen eine Gefahr für die Patient:innen Sicherheit dar. Strukturierte Abläufe können diese Gefahr mindern. Eine Möglichkeit, den Schichtwechsel strukturierter zu gestalten, ist die Verwendung einer Checkliste. Die Entwicklung praxisfähiger Checklisten ist jedoch eine Herausforderung, insbesondere im heutigen klinischen Um-feld, das durch hohen Arbeitsdruck und komplexe Abläufe gekennzeichnet ist. Um diesen Anforderun-gen zu begegnen, wurde in dieser Arbeit ein Safety-II-Ansatz verfolgt, bei dem eine Checkliste mittels der Functional Resonance Analysis Method (FRAM) entwickelt wurde. Die entwickelte Checkliste be-rücksichtigt verschiedene Aspekte wie die eigene Pflegeplanung, einen vereinfachten und individuel-len Ablauf, Unterstützung bei Störungen und die Überprüfung der Identität der Patient:innen. Die Checkliste wurde zudem im Rahmen einer «Room of Improvement»-Simulationsstudie getestet. Die Ergebnisse zeigten, dass die Checkliste eine solide Grundlage geschaffen hat, jedoch noch weitere Anpassungen notwendig sind, um eine optimale Integration in den klinischen Alltag zu gewährleisten und die Patient:innen Sicherheit nachhaltig zu verbessern.11 - Studentische Arbeit
