Can Outcomes of a Chat-Based Suicide Prevention Helpline Be Improved by Training Counselors in Motivational Interviewing? A Non-randomized Controlled Trial

Janssen, Wilco; Raak, Jeroen van; Lucht, Yannick van der; Ballegooijen, Wouter van; Mérelle, Saskia

doi:10.3389/fdgth.2022.871841

Cited by 5 publications

References 52 publications

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Effective suicide prevention interventions and their significance for the Dutch work field and the National Agenda on Suicide Prevention

Feltz-Cornelis

Jong

Bakker

et al. 2023

TSG Tijdschr Gezondheidswet

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SamenvattingDit artikel geeft een overzicht van effectieve suïcidepreventie-interventies en hun betekenis voor het Nederlandse werkveld en de Landelijke Agenda Suïcidepreventie. We bespreken een in 2020 gepubliceerde systematische review die het effect van preventieve interventies op suïcides en suïcidepogingen evalueerde, en geven een update van de ontwikkelingen daarna. Zestien onderzoeken met 252.932 participanten lieten zien dat er effectieve suïcidepreventie-interventies bestaan, zowel voor het voorkómen van suïcides, als van suïcidepogingen. Multi-level-interventies hebben grotere effecten dan uni-level-interventies. Vooral keteninterventies die nadrukkelijk de verbinding tussen identificatie van mensen met suïciderisico op populatieniveau en toegang tot specialistische zorg leggen, verdienen landelijke prioriteit. Een digitaal beslissingsondersteunend instrument voor hulpverleners en ketenpartners van alle niveaus kan uitkomst bieden. Dit instrument werd ontwikkeld binnen SUPREMOCOL, een onderzoek naar de effectiviteit van een systeeminterventie voor suïcidepreventie, uitgevoerd in de provincie Noord-Brabant, dat significante resultaten had in het voorkómen van suïcide. Het is het enige wetenschappelijk onderzoek in Nederland dat expliciet suïcide en suïcidepogingen als uitkomsten van een suïcidepreventie-interventie evalueert. In Nederland is dat nodig in het kader van de Landelijke Agenda Suïcidepreventie.

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Effective suicide prevention interventions and their significance for the Dutch work field and the National Agenda on Suicide Prevention

Feltz-Cornelis

Jong

Bakker

et al. 2023

TSG Tijdschr Gezondheidswet

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Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial

Pellemans,

Salmi,

Mérelle

et al. 2024

J Med Internet Res

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Background With the rise of computer science and artificial intelligence, analyzing large data sets promises enormous potential in gaining insights for developing and improving evidence-based health interventions. One such intervention is the counseling strategy motivational interviewing (MI), which has been found effective in improving a wide range of health-related behaviors. Despite the simplicity of its principles, MI can be a challenging skill to learn and requires expertise to apply effectively. Objective This study aims to investigate the performance of artificial intelligence models in classifying MI behavior and explore the feasibility of using these models in online helplines for mental health as an automated support tool for counselors in clinical practice. Methods We used a coded data set of 253 MI counseling chat sessions from the 113 Suicide Prevention helpline. With 23,982 messages coded with the MI Sequential Code for Observing Process Exchanges codebook, we trained and evaluated 4 machine learning models and 1 deep learning model to classify client- and counselor MI behavior based on language use. Results The deep learning model BERTje outperformed all machine learning models, accurately predicting counselor behavior (accuracy=0.72, area under the curve [AUC]=0.95, Cohen κ=0.69). It differentiated MI congruent and incongruent counselor behavior (AUC=0.92, κ=0.65) and evocative and nonevocative language (AUC=0.92, κ=0.66). For client behavior, the model achieved an accuracy of 0.70 (AUC=0.89, κ=0.55). The model’s interpretable predictions discerned client change talk and sustain talk, counselor affirmations, and reflection types, facilitating valuable counselor feedback. Conclusions The results of this study demonstrate that artificial intelligence techniques can accurately classify MI behavior, indicating their potential as a valuable tool for enhancing MI proficiency in online helplines for mental health. Provided that the data set size is sufficiently large with enough training samples for each behavioral code, these methods can be trained and applied to other domains and languages, offering a scalable and cost-effective way to evaluate MI adherence, accelerate behavioral coding, and provide therapists with personalized, quick, and objective feedback.

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The most effective interventions during online suicide prevention chats: Machine Learning Study (Preprint)

Salmi,

Mérelle,

Gilissen

et al. 2024

JMIR Mental Health

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Can Outcomes of a Chat-Based Suicide Prevention Helpline Be Improved by Training Counselors in Motivational Interviewing? A Non-randomized Controlled Trial

Cited by 5 publications

References 52 publications

Effective suicide prevention interventions and their significance for the Dutch work field and the National Agenda on Suicide Prevention

Effective suicide prevention interventions and their significance for the Dutch work field and the National Agenda on Suicide Prevention

Automated Behavioral Coding to Enhance the Effectiveness of Motivational Interviewing in a Chat-Based Suicide Prevention Helpline: Secondary Analysis of a Clinical Trial

The most effective interventions during online suicide prevention chats: Machine Learning Study (Preprint)

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