The contributors to chronic stress in the workplace have been well established in existing research. How everyday stressors translate to long-term risk factors for chronic stress remains an open question, however. In our literature review, we tackled the question of which everyday workplace experiences cause stress as their immediate consequence. We were interested in what is termed as acute, periodic, episodic, or everyday stress, that is, non-chronic types of stress. To capture day-to-day variations in these experiences, we included only studies that assessed stress repeatedly or monitored it continuously, such as by using ecological momentary assessment (EMA). A search strategy was devised and applied in the following databases: CINAHL, Embase, ERIC, PsycARTICLES, PubMed, Scopus, and Web of Science. We deduplicated records in EndNote, assessed the titles and abstracts against eligibility criteria by using Rayyan and selected a minority of them for full-text screening. A subset of papers was included in the final review and evaluated for quality using the QualSyst tool at study level and using the GRADE approach at body of evidence level. The systematic review has been registered on PROSPERO (Registration Number: CRD42018105355). We will compile a list of (non-chronic) stressors, i.e. daily experiences that elicited stress in the workplace. In case they are reported systematically enough throughout the literature, they will be rated in terms of their importance. Finally, subgroup comparisons will be carried out from the point of view of occupational classification.
COBISS.SI-ID: 13631235
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 STRAW project adds novel aspects to occupational stress research among academic staff 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.
COBISS.SI-ID: 45258499