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While the importance of supervisors’ communication for the successful implementation of organisational change is often emphasised, evidence regarding the actual dimensions of change communication and the reaction of change recipients is scarce. As communication is highly context and culture specific, this study included participants from Germany and India. To meet these research aims, we conducted focus group discussions (FGD) with overall 50 graduate students with work experiences from Germany and India. We identified key dimensions of informative, empathic and participatory change communication that are of high importance for change recipients. The German and Indian participants had similar expectations regarding the informative and empathic dimensions of change communication. However, interestingly, their assessment of the actual participation differed substantially, with consequences for their emotional responses. The findings emphasise the crucial role of supervisors’ communication for employees’ emotions in times of change. Supervisors thereby need to consider the diverse dimensions of their communication and stimulate employees’ expectations regarding information, empathy and participation with different communicative activities and approaches. This study provides important insights into organisational change communication by analysing its key dimensions, linking it to employees’ emotions and considering the cultural context.
Forschungsfrage: In welchem Umfang werden Persönlichkeitseigenschaften in Stellenanzeigen verwendet, die eher als maskulin wahrgenommen werden?
Methodik: Wir nutzen einen Pool von deutschsprachigen Stellenanzeigen von Dax-30-Unternehmen, mittelständischen Unternehmen und des öffentlichen Sektors in den Bereichen Personalwesen (N=2.086) und Softwareentwicklung (N=1.811).
Praktische Implikationen: Unternehmen, die eine diverse Belegschaft anstreben, sollten nicht einseitig vor allem maskuline Persönlichkeitseigenschaften fordern. Die Eigenschaften signalisieren auch die Unternehmenskultur.
Für die Versorgungsforschung ist wichtig, dass verteilte und heterogene Daten so integriert werden, dass sie offen für neue Analyse-Anforderungen und leicht um neue Datenquellen erweiterbar sind. Für die Integration von Versorgungsdaten werden bislang hauptsächlich Data-Warehouses eingesetzt, die Daten dimensional oder als Entity-Attribute-Value-Struktur (EAV) modellieren. Diese Datenmodelle sind jedoch entweder unflexibel oder weisen ein zu geringes Maß an Datenorganisation auf, was longitudinale Analysen erschwert. Wir haben den EAV-Ansatz um die Data-Vault-Modellierung ergänzt und damit die Datenstrukturen der Krankenhaus-Qualitätsberichte des Gemeinsamen Bundesausschusses (G-BA) modelliert sowie die Daten der Jahre 2011 bis 2015 integriert. Dies ermöglicht eine Historisierung der Metadaten für Merkmale, insbesondere der Qualitätsindikatoren, sowie ein hohes Maß an Erweiterbarkeit gegenüber neuen heterogenen Datenquellen. Der vorgeschlagene Ansatz erlaubt es, den Abstraktionsgrad für die zu modellierenden Entitäten frei zu wählen, so dass auch ein vollständig generisches EAV-Modell mit historisierten Metadaten erstellt werden kann.
Background: For more than 30 years, there has been close cooperation between Japanese and German scientists with regard to information systems in health care. Collaboration has been formalized by an agreement between the respective scientific associations. Following this agreement, two joint workshops took place to explore the similarities and differences of electronic health record systems (EHRS) against the background of the two national healthcare systems that share many commonalities.
Objectives: To establish a framework and requirements for the quality of EHRS that may also serve as a basis for comparing different EHRS.
Methods: Donabedian's three dimensions of quality of medical care were adapted to the outcome, process, and structural quality of EHRS and their management. These quality dimensions were proposed before the first workshop of EHRS experts and enriched during the discussions.
Results: The Quality Requirements Framework of EHRS (QRF-EHRS) was defined and complemented by requirements for high quality EHRS. The framework integrates three quality dimensions (outcome, process, and structural quality), three layers of information systems (processes and data, applications, and physical tools) and three dimensions of information management (strategic, tactical, and operational information management).
Conclusions: Describing and comparing the quality of EHRS is in fact a multidimensional problem as given by the QRF-EHRS framework. This framework will be utilized to compare Japanese and German EHRS, notably those that were presented at the second workshop.