UGent @ Work seminarie #5

Wanneer
28-10-2021 van 11:30 tot 13:00
Waar
https://teams.microsoft.com/l/meetup-join/19%3ameeting_NjQ4NmU3NGMtM2ViZS00M2IzLWJkNjQtOWJjMDc5MTkyZWZi%40thread.v2/0?context=%7b%22Tid%22%3a%22d7811cde-ecef-496c-8f91-a1786241b99c%22%2c%22Oid%22%3a%227585cebf-71f6-4efc-9d46-0e49e9a74dd1%22%7d
Voertaal
Engels
Door wie
Brecht Neyt
Contact
Brecht.Neyt@UGent.be

Link to MS Teams meeting

Click here to join the seminar

Programme

11.30h–11.55h Presentation Larissa Bolliger
11.55h–12.05h Feedback Anneleen Mortier (discussant)
12.05h–12.15h Feedback other attendees

12.15h–12.45h Presentation Bert George
12.45h–13.00h Feedback other attendees

Presentation Larissa Bolliger

Title: STRAW Project: Disentangling the sources and context of day-to-day STRess At Work – how job strain influences our work-life interference.

Authors: Larissa Bolliger, Junoš Lukan, Mitja Luštrek, Dirk De Bacquer, and Els Clays

Abstract (protocol): Several studies have reported on increasing psychosocial stress in academia due to work environment risk factors like job insecurity, work-family conflict, research grant applications, and high workload. The STRAWproject adds novel aspects to occupational stress research among academic sta by measuring day-to-day stress in their real-world work environments over 15 working days. Work environment risk factors, stress outcomes, health-related behaviors, and work activities were measured repeatedly via an ecological momentary assessment (EMA), specially developed for this project. These results were combined with continuously tracked physiological stress responses using wearable devices and smartphone sensor and usage data. These data provide information on workplace context using our self-developed Android smartphone app. The data were analyzed using two approaches: (1) multilevel statistical modelling for repeated data to analyze relations between work environment risk factors and stress outcomes on a within- and between-person level, based on EMA results and a baseline screening, and (2) machine-learning focusing on building prediction models to develop and evaluate acute stress detection models, based on physiological data and smartphone sensor and usage data. Linking these data collection and analysis approaches enabled us to disentangle and model sources, outcomes, and contexts of occupational stress in academia.

Discussant: dr. Anneleen Mortier

Slides: TBA

Presentation Bert George

Title: The Learning Public Organization: The Construct, Its Measurement and Barriers to Implementation

Authors: TBA

Abstract: TBA

Slides: TBA