Natalie
Shapira

Change in Language Session-by-Session During Psychotherapy
BIU, 4Good
Natalie Shapira

Natalie
Shapira

Change in Language Session-by-Session During Psychotherapy

BIU, 4Good

Natalie Shapira

Bio

Natalie is a computer science researcher combining AI methods (ML, DL,NLP) and clinical psychology. She serves as a mentor at Google for Startups. Formerly, worked at IBM Research Labs.

Bio

Natalie is a computer science researcher combining AI methods (ML,
DL,NLP) and clinical psychology. She serves as a mentor at Google for Startups.
Formerly, worked at IBM Research Labs.

Abstract

Routine monitoring of clients’ functioning has shown great promise in contributing to therapeutic progress. However, most studies rely on clients’ self-report measures which may be affected by clients’ insight regarding their symptoms as well as their motivation to complete the questionnaires. Self-report measures are usually completed before or after the session and thus are limited in their ability to explore the structure of verbal exchanges between the clients and the therapists that are the essence of psychotherapy.

 

The remarkable potential of automated text analytic techniques to explore the important information hidden in the huge amount of words spoken in psychotherapy sessions, had been recognized recently by a few psychotherapy researchers (e.g., Imel et al., 2016). Research outside the clinical domain that used advanced text analytic techniques, repeatedly recognized several textual markers, such as higher use of first person singular, higher use of negative emotional words and lower use of positive emotional words, to be associated with levels of distress. However, these previous studies have tended to focus on a single time point and did not examine change over time. The current study aims to use advanced text analytic techniques to examine whether change in textual markers is associated with changes in clients’ well-being.

Abstract

Routine monitoring of clients’ functioning has shown great promise in contributing to therapeutic progress. However, most studies rely on clients’ self-report measures which may be affected by clients’ insight regarding their symptoms as well as their motivation to complete the questionnaires. Self-report measures are usually completed before or after the session and thus are limited in their ability to explore the structure of verbal exchanges between the clients and the therapists that are the essence of psychotherapy.

 

The remarkable potential of automated text analytic techniques to explore the important information hidden in the huge amount of words spoken in psychotherapy sessions, had been recognized recently by a few psychotherapy researchers (e.g., Imel et al., 2016). Research outside the clinical domain that used advanced text analytic techniques, repeatedly recognized several textual markers, such as higher use of first person singular, higher use of negative emotional words and lower use of positive emotional words, to be associated with levels of distress. However, these previous studies have tended to focus on a single time point and did not examine change over time. The current study aims to use advanced text analytic techniques to examine whether change in textual markers is associated with changes in clients’ well-being.

Planned Agenda

Planned Agenda