4.3 Article

A New Academic Quality at Work Tool (AQ@workT) to Assess the Quality of Life at Work in the Italian Academic Context

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MDPI
DOI: 10.3390/ijerph19063724

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job demands-resources model; quality of life in academia; validation; academic teaching staff; assessment tool

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The present study introduces a reliable tool, AQ@workT, to assess the quality of work life in academics within the Italian university sector. Developed by the QoL@Work research team, AQ@workT is grounded in the job demands-resources model and its psychometric properties were supported in three studies with a large sample of lecturers, researchers, and professors. The findings show that AQ@workT is a valuable instrument for university management to improve quality of life, manage work-related stress, and mitigate potential harm to academics.
The present study provides evidence for a valid and reliable tool, the Academic Quality at Work Tool (AQ@workT), to investigate the quality of life at work in academics within the Italian university sector. The AQ@workT was developed by the QoL@Work research team, namely a group of expert academics in the field of work and organizational psychology affiliated with the Italian Association of Psychologists. The tool is grounded in the job demands-resources model and its psychometric properties were assessed in three studies comprising a wide sample of lecturers, researchers, and professors: a pilot study (N = 120), a calibration study (N = 1084), and a validation study (N = 1481). Reliability and content, construct, and nomological validity were supported, as well as measurement invariance across work role (researchers, associate professors, and full professors) and gender. Evidence from the present study shows that the AQ@workT represents a useful and reliable tool to assist university management to enhance quality of life, to manage work-related stress, and to mitigate the potential for harm to academics, particularly during a pandemic. Future studies, such as longitudinal tests of the AQ@workT, should test predictive validity among the variables in the tool.

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