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Towards Sustainable Workplace Mental Health: A Novel Approach to Early Intervention and Support

David W. Vinson, Mihael Arcan, David-Paul Niland, Fionn Delahunty

TL;DR

This work introduces a real-time, privacy-preserving stress detection chatbot embedded in an employee experience platform to proactively support mental health in the workplace. It combines multilingual translation via a transformer, ensemble NER-based PII anonymization, and a linguistic-biomarker stress detector that flags internal chats and initiates chatbot outreach when stress indicators exceed a threshold. In an 18-week pilot with 416 employees across Colombia and the Philippines, the system flagged 1.99% of messages as stressed and generated measurable engagement, revealing demographic and tenure patterns in stress signals and engagement dynamics. The study demonstrates feasibility and provides insight into implementing proactive, scalable mental health interventions in corporate settings, with implications for sustainable workplace practices and future optimization of alert timing and targeted outreach.

Abstract

Employee well-being is a critical concern in the contemporary workplace, as highlighted by the American Psychological Association's 2021 report, indicating that 71% of employees experience stress or tension. This stress contributes significantly to workplace attrition and absenteeism, with 61% of attrition and 16% of sick days attributed to poor mental health. A major challenge for employers is that employees often remain unaware of their mental health issues until they reach a crisis point, resulting in limited utilization of corporate well-being benefits. This research addresses this challenge by presenting a groundbreaking stress detection algorithm that provides real-time support preemptively. Leveraging automated chatbot technology, the algorithm objectively measures mental health levels by analyzing chat conversations, offering personalized treatment suggestions in real-time based on linguistic biomarkers. The study explores the feasibility of integrating these innovations into practical learning applications within real-world contexts and introduces a chatbot-style system integrated into the broader employee experience platform. This platform, encompassing various features, aims to enhance overall employee well-being, detect stress in real time, and proactively engage with individuals to improve support effectiveness, demonstrating a 22% increase when assistance is provided early. Overall, the study emphasizes the importance of fostering a supportive workplace environment for employees' mental health.

Towards Sustainable Workplace Mental Health: A Novel Approach to Early Intervention and Support

TL;DR

This work introduces a real-time, privacy-preserving stress detection chatbot embedded in an employee experience platform to proactively support mental health in the workplace. It combines multilingual translation via a transformer, ensemble NER-based PII anonymization, and a linguistic-biomarker stress detector that flags internal chats and initiates chatbot outreach when stress indicators exceed a threshold. In an 18-week pilot with 416 employees across Colombia and the Philippines, the system flagged 1.99% of messages as stressed and generated measurable engagement, revealing demographic and tenure patterns in stress signals and engagement dynamics. The study demonstrates feasibility and provides insight into implementing proactive, scalable mental health interventions in corporate settings, with implications for sustainable workplace practices and future optimization of alert timing and targeted outreach.

Abstract

Employee well-being is a critical concern in the contemporary workplace, as highlighted by the American Psychological Association's 2021 report, indicating that 71% of employees experience stress or tension. This stress contributes significantly to workplace attrition and absenteeism, with 61% of attrition and 16% of sick days attributed to poor mental health. A major challenge for employers is that employees often remain unaware of their mental health issues until they reach a crisis point, resulting in limited utilization of corporate well-being benefits. This research addresses this challenge by presenting a groundbreaking stress detection algorithm that provides real-time support preemptively. Leveraging automated chatbot technology, the algorithm objectively measures mental health levels by analyzing chat conversations, offering personalized treatment suggestions in real-time based on linguistic biomarkers. The study explores the feasibility of integrating these innovations into practical learning applications within real-world contexts and introduces a chatbot-style system integrated into the broader employee experience platform. This platform, encompassing various features, aims to enhance overall employee well-being, detect stress in real time, and proactively engage with individuals to improve support effectiveness, demonstrating a 22% increase when assistance is provided early. Overall, the study emphasizes the importance of fostering a supportive workplace environment for employees' mental health.
Paper Structure (13 sections, 6 figures)

This paper contains 13 sections, 6 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Results
  5. Employee’s Stress
  6. Demographic Stress
  7. Defining a ‘Stressed’ Cohort
  8. Engagement
  9. Engaged cohort
  10. Type of Engagement
  11. Depth of Engagement
  12. Self-Reporting
  13. Conclusions

Figures (6)

  • Figure 1: Text communication with redacted PII for confidentiality.
  • Figure 2: Histogram of the total number of messages per 309 employees who were flagged as having at least one stressed message over the 18 week pilot (left side). Histogram of the total number of messages per 107 employees who never had a stressed message over the 18 week pilot (right side).
  • Figure 3: Histogram of percentage of stressed messages by employee.
  • Figure 4: Distribution representation of repeated agent responses.
  • Figure 5: The total number of responses by category.
  • ...and 1 more figures