Identifying {Early Maladaptive Schemas} from Mental Health Question Texts
Submission Type: Regular Short Paper
Submission Track: NLP Applications
Submission Track 2: Theme Track: Large Language Models and the Future of NLP
Keywords: Mental Health, Schema Therapy, Early Maladaptive Schema, Personality Disorders, Classification
TL;DR: Predicting EMS (Early Maladaptive Schema) labels for mental health QA forum posts
Abstract: In Psychotherapy, {maladaptive schemas}-- negative perceptions that {an
individual has of the self, others, or the world that endure despite objective reality}--
often lead to resistance to treatments and relapse of mental health issues such as depression, anxiety, panic attacks etc. Identification of early maladaptive schemas (EMS) is thus a crucial
step during Schema Therapy-based counseling sessions,
where patients
go through a detailed and lengthy EMS questionnaire. However, such an approach is not practical in "offline" counseling scenarios, such as community QA forums which are gaining popularity for people seeking mental health support. In this paper, we
investigate both LLM (Large Language Models) and non-LLM approaches for identifying EMS labels using resources from Schema Therapy.
Our evaluation indicates that
recent LLMs can be effective for identifying EMS but their predictions lack explainability and are too sensitive to precise `prompts'.
Both LLM and non-LLM methods are unable to reliably address the {null} cases, i.e. cases with no EMS labels. However, we posit that the two approaches show complementary properties and together,
they can be used to further devise techniques
for EMS identification.
Submission Number: 2380
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