The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach

This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysis.

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Publication Details

Journal
JMIR Mental Health
Published
2024-08-11
DOI
https://doi.org/10.2196/57362
Citations
4
Primary Topic
Mental Health via Writing
Type
article
Field-Weighted Citation Impact
2.57
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article

The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach

Sandjai Bhulai, Salim Salmi, Renske Gilissen, Rob van der Mei et al.
4 citations
JMIR Mental Health
Mental Health via Writing
2.57
article

The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach

Sandjai Bhulai, Salim Salmi, Renske Gilissen, Rob van der Mei, Saskia Mérelle
article en
4 citations

Abstract

This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions, positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysis.

JMIR Mental HealthVol. 11
Centrum Wiskunde & Informatica (NL), GGD Amsterdam (NL), Vrije Universiteit Amsterdam (NL)
Gender equality
Openalex Percentile: Top 11%
Mental Health via Writing
2.57
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