AI-based early mental health screening for local public officials

Area of Science:

• Public Health
• Occupational Health
• Artificial Intelligence in Healthcare

Background:

• Local public officials face significant occupational stress and burnout from demanding workloads and organizational cultures.
• Traditional self-report mental health assessments are limited by subjectivity and stigma, hindering early intervention.
• Artificial intelligence (AI) offers a potential solution for accessible and stigma-free mental health screening.

Purpose of the Study:

• To evaluate the acceptability and perceived usefulness of an AI-based mental health screening system for local public officials.
• To assess officials' trust in AI compared to self-report measures and their willingness to engage with AI screening.
• To explore the potential of AI to mitigate stigma associated with mental health services.

Main Methods:

• A two-stage survey was administered to 126 local public officials.
• Stage one utilized standardized instruments for job stress and stigma perception.
• Stage two involved 30 participants completing both self-report and GPT-based AI assessments, followed by satisfaction evaluations.

Main Results:

• Officials reported high job stress, with younger individuals being more vulnerable.
• Despite limited prior AI experience, 73.8% trusted AI results more than self-reports, and 84.9% were willing to participate regularly.
• AI screening was highly rated for usability and efficiency, with 81.7% agreeing it could reduce stigma; trust in AI and perceived stigma reduction predicted acceptance.

Conclusions:

• AI-based screening presents a promising, stigma-free tool for assessing public officials' mental health.
• High trust and willingness to engage suggest AI's potential for early intervention in this population.
• Sustainable implementation requires institutional safeguards and further algorithm validation.