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• W4205894866 abstract "<sec> <title>BACKGROUND</title> Stress, burnout, and mental health problems such as depression and anxiety are common, and can significantly impact workplaces through absenteeism and reduced productivity. To address this issue, organizations must first understand the extent of the difficulties by mapping the mental health of their workforce. Online surveys are a cost-effective and scalable approach to achieve this but typically have low response rates, in part due to a lack of interactivity. Chatbots offer one potential solution, enhancing engagement through simulated natural human conversation and use of interactive features. </sec> <sec> <title>OBJECTIVE</title> The aim of this study was to explore if a text-based chatbot is a feasible approach to engage and motivate employees to complete a workplace mental health assessment. This paper describes the design process and results of a pilot implementation. </sec> <sec> <title>METHODS</title> A fully automated chatbot (“Viki”) was developed to evaluate employee risks of suffering from depression, anxiety, stress, insomnia, burnout, and work-related stress. Viki uses a conversation style and gamification features to enhance engagement. A cross-sectional analysis was performed to gain first insights of a pilot implementation within a small to medium–sized enterprise (120 employees). </sec> <sec> <title>RESULTS</title> The response rate was 64.2% (77/120). In total, 98 employees started the assessment, 77 of whom (79%) completed it. The majority of participants scored in the mild range for anxiety (20/40, 50%) and depression (16/28, 57%), in the moderate range for stress (10/22, 46%), and at the subthreshold level for insomnia (14/20, 70%) as defined by their questionnaire scores. </sec> <sec> <title>CONCLUSIONS</title> A chatbot-based workplace mental health assessment seems to be a highly engaging and effective way to collect anonymized mental health data among employees with response rates comparable to those of face-to-face interviews. </sec>" @default.
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• W4205894866 date "2020-06-21" @default.
• W4205894866 modified "2023-10-16" @default.
• W4205894866 title "Chatbot-Based Assessment of Employees’ Mental Health: Design Process and Pilot Implementation (Preprint)" @default.
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• W4205894866 doi "https://doi.org/10.2196/preprints.21678" @default.
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