Detecting Therapist Burnout and Flourishing From Brief Speech: A Machine‐Learning Approach

ABSTRACT Background Burnout is pervasive among therapists and is associated with impaired well‐being and reduced clinical effectiveness. Although flourishing and character strengths may function as protective factors, intervention implementation is limited by a lack of feasible, scalable methods for monitoring therapist well‐being. Machine‐learning (ML) approaches may be suited to address these limitations. Aims This proof‐of‐concept study examined the use of a transformer‐based natural language processing (NLP) model to detect therapist burnout, flourishing, mental health continuum, employee engagement and character strengths (humility, gratitude and forgiveness) from automatically transcribed therapists' verbal responses. Materials and Methods Participants ( n = 365) included licensed and training therapists, who completed self‐report measures of burnout, flourishing, mental health continuum, employee engagement and character strengths (humility, gratitude and forgiveness). A transformer‐based RoBERTa model was trained, tuned and evaluated using separate training, validation and test sets, with performance assessed using Spearman rank correlations with permutation tests. Results Moderate correlations were observed for burnout ( ρ = 0.456), mental health continuum ( ρ = 0.46) and flourishing ( ρ = 0.387). Discussion These findings suggest that transformer‐based NLP models might be able to detect therapist burnout, flourishing and some positive functioning indicators relevant to organisational well‐being models. Limitations include the relatively small and potentially unrepresentative cross‐sectional sample, possible transcription and prompt‐related measurement error, and the absence of external validation, warranting cautious interpretation of these findings as preliminary evidence of feasibility rather than as definitive estimates of detection performance. Research should also examine ethical concerns related to privacy, bias, misclassification and potential misuse. Conclusion This study provides preliminary evidence supporting the use of transformer‐based NLP models for the detection of therapist burnout, flourishing and other positive functioning indicators.