2021-08-23, Füllemann, Désirée, Schönholzer, Tanja, Flükiger, Nicole, Nauser, Ottilia, Jenny, Gregor J., Jensen, Regina, Krause, Andreas

2020-08-18, Fridrich, Annemarie, Bauer, Georg F., Jenny, Gregor J.

2021-08-23, Füllemann, Désirée, Schönholzer, Tanja, Flükiger, Nicole, Nauser, Ottilia, Jenny, Gregor J., Jensen, Regina, Krause, Andreas

2020-02-01, Lehmann, Anja I., Brauchli, Rebecca, Jenny, Gregor J., Füllemann, Désirée, Bauer, Georg F.

This study aimed to examine how far group-level psychosocial and affective factors, as a relevant context, predict outcome expectancy as a process appraisal of an organizational health intervention. For this purpose, data from a university hospital (N = 250 representatives from 29 nursing wards) were collected. Participants took part in an intervention consisting of 4-day workshops designed to improve psychosocial working conditions. Employee surveys covered baseline psychosocial (job demands and job resources) and affective aspects (valence and positive and negative activation) as context variables. At the end of the workshops, participants evaluated the intervention process with the outcome expectancy scale. Applying a multilevel approach, the results indicated that both baseline psychosocial characteristics (job resources, in particular managerial support) and baseline affective factors (valence) as relevant context characteristics were related to the appraisal of the intervention process (outcome expectancy). The post hoc mediation analysis further showed that the affective context (valence) mediated the relation between job resources (managerial support) and outcome expectancy. There was no relation between job demands and outcome expectancy as well as between negative activation and outcome expectancy. This study shows that already healthy contexts with good psychosocial working conditions and well-being relate to a beneficial intervention process. Specifically, this study highlights the essential role of affects that influence process appraisals. These affects are, in turn, influenced by the psychosocial context. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

2021-08-23, Füllemann, Désirée, Schönholzer, Tanja, Flükiger, Nicole, Nauser, Ottilia, Jenny, Gregor J., Jensen, Regina, Krause, Andreas

Repräsentative Erhebung 2020 und Trends seit 2016: Das BGM-Monitoring von Gesundheitsförderung Schweiz ist eine periodische Erhebung ausgewählter Indikatoren und liefert repräsentative Informationen über die Verbreitung von betrieblichem Gesundheitsmanagement (BGM) in Betrieben in der Schweiz. Im vorliegenden Arbeitspapier werden die Ergebnisse der zweiten repräsentativen Erhebung im Jahr 2020 dargestellt. Durch den Vergleich der beiden Befra-gungen 2016 und 2020 werden Trends (Veränderungen) in der Verbreitung von BGM abgebildet. Befragt wurde eine repräsentative Stichprobe von 791 Industrie- und Dienstleistungsbetrieben mit mindestens 50 Mitarbeitenden in der deutsch-, französisch- und italienischsprachigen Schweiz.

2019-11-27, Jenny, Gregor J., Bauer, Georg F., Füllemann, Désirée, Broetje, Sylvia, Brauchli, Rebecca

Objectives Practitioners and organizational leaders are calling for practical ways to explain and monitor factors that affect workplace health and productivity. This article builds on the well‐established Job Demands‐Resources (JD‐R) model and proposes an empirically tested ratio that aggregates indicators of job resources and demands. In this study, we calculate a ratio of generalizable job resources and demands derived from the JD‐R model and then translate the ratio into the language of company stakeholders. Methods We calculated a ratio based on measures applied in a large stress management intervention study (n = 2983) and report the findings from cross‐sectional analysis with health and productivity outcomes from same‐source and separate‐source data. Results Findings showed a strong and unambiguous increase in health and productivity measures with each step of increase in the ratio. Loss in explained variance due to aggregation of two factors into a single ratio is small for measures which are known to be predicted by both factors simultaneously. Conclusions A translation and visualization of the ratio that is accessible to practitioners and organizational leaders is presented and its use in companies discussed.