Recurrent Contact Prediction in Crisis Chat Counseling: A Preprocessed and Anonymized Secondary Data Analysis

Introduction

In the realm of mental health care, crisis chat counseling has emerged as a vital lifeline for individuals seeking support in times of distress. Understanding the factors that influence whether an individual will reach out for help again is crucial for optimizing service provision. This study delves into the prediction of recurrent contact in crisis chat counseling through an in-depth analysis of preprocessed and anonymized secondary data.

Background

Study Design and Intervention

This study utilized routine-care data from krisenchat, a German non-profit organization providing free 24/7 chat counseling for young people up to age 24. The analysis focused on individuals who contacted krisenchat for the first time between October 2021 and December 2022.

Sample

The sample consisted of 18,871 unique chatters who met the inclusion criteria of having received a consultation, defined as at least three counselor messages and ten exchanged messages.

Outcome Variable

The primary outcome of interest was recurrence of chat contact within 188 days after the first consultation. Approximately 43.1% of the chatters recontacted within this period, highlighting the need for improved understanding of factors influencing continued engagement.

Data Preprocessing and Anonymization

To ensure the privacy and anonymity of the vulnerable sample, extensive data preprocessing and anonymization techniques were employed. Counselors marked personal identifiable information for deletion, and names and city names were replaced with tokens. Additionally, conversations were stemmed, words were shuffled randomly, and words present in less than five chats were removed. These measures safeguarded the confidentiality of the participants while preserving the integrity of the data for analysis.

Explainability

SHAP Values

– SHAP (SHapley Additive Explanations) values quantified the average contribution of each word stem to each prediction. Words indicating emotional distress, such as “sad,” “alone,” and “despair,” had the highest positive SHAP values.

Co-Occurrence Analysis

– Co-occurrence analysis revealed that word stems associated with emotional distress frequently co-occurred with stems related to self-harm and suicidal thoughts. This highlights the importance of recognizing and addressing these concerns in crisis chat counseling.

Clustering

– Word2Vec clustering identified three distinct clusters of word stems: (1) emotional distress, (2) self-harm and suicidal thoughts, and (3) positive coping mechanisms. This clustering provides insights into the complex and multifaceted nature of crisis chat conversations.

Conclusion

Our study demonstrates the feasibility and effectiveness of using machine learning to predict recurrent contact in crisis chat counseling. The text-based algorithm outperformed the baseline and provided valuable insights into the language used by chatters and counselors.

This research has important implications for improving crisis chat counseling services. By identifying chatters at risk of recurrence, counselors can proactively reach out to offer additional support. The explainability techniques employed in our study can also help counselors better understand the needs of chatters and tailor their interventions accordingly.

Further research should explore the use of machine learning to predict other outcomes in crisis chat counseling, such as the severity of distress or the likelihood of a positive resolution. Additionally, future studies should investigate the effectiveness of using machine learning to personalize the content of chat interventions.

By harnessing the power of data and technology, we can improve the quality and effectiveness of crisis chat counseling services and ultimately help more young people in need.