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Detecting premature departure in online text-based counseling using logic-based pattern matching.

Yucan Xu1, Christian S Chan2, Christy Tsang1

  • 1Centre for Suicide Research and Prevention, The University of Hong Kong, Pokfulam, Hong Kong.

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Users may prematurely depart from online counseling sessions. This study developed a model to systematically identify these departures, finding they correlate with lower user satisfaction and session helpfulness.

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Area of Science:

  • Digital mental health
  • Human-computer interaction
  • Clinical psychology

Background:

  • Online text-based counseling presents unique challenges for session closure compared to face-to-face interactions.
  • Premature departure from online counseling sessions is an understudied phenomenon that may indicate user risk or dissatisfaction.
  • A systematic method is needed to identify premature departures in online counseling.

Purpose of the Study:

  • To develop a systematic method for identifying premature departures in online text-based counseling using logic-based pattern matching.
  • To validate the significance of premature departure by assessing its association with user satisfaction and perceived session helpfulness.

Main Methods:

  • A classification model was developed and tested on 575 human-annotated online counseling sessions.
  • The model was trained on 80% of the data and validated on 20%, then applied to a full dataset of 34,821 sessions.
  • User satisfaction was compared between premature departure and completed sessions using post-session survey data.

Main Results:

  • The classification model achieved high accuracy (97% F1 score on training, 92% on test sets) in identifying premature departures.
  • Over 43% of sessions (15,150 out of 34,821) were classified as premature departures.
  • Premature departure was significantly linked to lower perceived helpfulness and effectiveness in distress reduction, with fewer users completing post-session surveys.

Conclusions:

  • This study presents the first systematic and accurate model for identifying premature departures in online text-based counseling.
  • The developed model can be adapted for use in other contexts to improve risk management and service quality.
  • Accurate identification of premature departures can aid in service evaluation and enhance user support in digital mental health platforms.