A PRELIMINARY STUDY OF AI-BASED PSYCHIATRIC COUNSELLING TO MANAGE EMPLOYEES STRESS AND TO ASSESS WORKPLACE MENTAL HEALTH BY THE END OF THIS DECADE (2030)

Background: Workplace stress, anxiety, burnout and psychological distress are important concerns affecting employee well-being and organisational performance. The increasing use of artificial intelligence (AI) in digital mental health support offers opportunities for accessible and timely assistance, while raising concerns regarding privacy, informed consent, algorithmic bias, clinical safety, accountability and human oversight. This study aims to examine the potential role and responsible integration of AI-based psychiatric counselling for managing employee stress and supporting workplace mental health by 2030. Materials and Methods: A mixed-methods research design will be used, combining quantitative survey data with qualitative interviews. The quantitative component will include approximately 350–450 employees, while the qualitative component will involve approximately 25–35 interviews with HR professionals, organisational leaders, EAP professionals and mental health practitioners. Data will assess perceived stress, anxiety, burnout, psychological safety, trust, confidentiality, privacy concerns, AI counselling acceptance and willingness to use AI-based mental health support. Quantitative data will be analysed using descriptive statistics, reliability testing, correlation, regression, t-tests, ANOVA and appropriate mediation or moderation analyses. Qualitative data will be analysed thematically. Results: The study is expected to identify factors influencing employee acceptance and trust in AI-based psychiatric counselling and to examine the role of privacy, human oversight, psychological safety and organisational support in its responsible adoption. Conclusion: AI-based psychiatric counselling may serve as a supportive component of workplace mental health systems, particularly for early support, psychoeducation, self-help and triage. However, it should complement rather than replace qualified mental health professionals. Responsible implementation will require privacy safeguards, informed consent, clinical oversight, transparency, fairness, accountability and clear referral pathways.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-13
DOI
https://doi.org/10.5281/zenodo.23333736
Primary Topic
Digital Mental Health Interventions
Type
article
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article

A PRELIMINARY STUDY OF AI-BASED PSYCHIATRIC COUNSELLING TO MANAGE EMPLOYEES STRESS AND TO ASSESS WORKPLACE MENTAL HEALTH BY THE END OF THIS DECADE (2030)

Dillip Kumar Dash
Zenodo (CERN European Organization for Nuclear Research)
Digital Mental Health Interventions
article

A PRELIMINARY STUDY OF AI-BASED PSYCHIATRIC COUNSELLING TO MANAGE EMPLOYEES STRESS AND TO ASSESS WORKPLACE MENTAL HEALTH BY THE END OF THIS DECADE (2030)

Dillip Kumar Dash
article en

Abstract

Background: Workplace stress, anxiety, burnout and psychological distress are important concerns affecting employee well-being and organisational performance. The increasing use of artificial intelligence (AI) in digital mental health support offers opportunities for accessible and timely assistance, while raising concerns regarding privacy, informed consent, algorithmic bias, clinical safety, accountability and human oversight. This study aims to examine the potential role and responsible integration of AI-based psychiatric counselling for managing employee stress and supporting workplace mental health by 2030. Materials and Methods: A mixed-methods research design will be used, combining quantitative survey data with qualitative interviews. The quantitative component will include approximately 350–450 employees, while the qualitative component will involve approximately 25–35 interviews with HR professionals, organisational leaders, EAP professionals and mental health practitioners. Data will assess perceived stress, anxiety, burnout, psychological safety, trust, confidentiality, privacy concerns, AI counselling acceptance and willingness to use AI-based mental health support. Quantitative data will be analysed using descriptive statistics, reliability testing, correlation, regression, t-tests, ANOVA and appropriate mediation or moderation analyses. Qualitative data will be analysed thematically. Results: The study is expected to identify factors influencing employee acceptance and trust in AI-based psychiatric counselling and to examine the role of privacy, human oversight, psychological safety and organisational support in its responsible adoption. Conclusion: AI-based psychiatric counselling may serve as a supportive component of workplace mental health systems, particularly for early support, psychoeducation, self-help and triage. However, it should complement rather than replace qualified mental health professionals. Responsible implementation will require privacy safeguards, informed consent, clinical oversight, transparency, fairness, accountability and clear referral pathways.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 9%
Digital Mental Health Interventions
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