Predicting Readiness To Engage In Psychotherapy From Self-Referral Forms: A Natural Language Processing Analysis Of Clinical Documents - Info and Reading Options
By Xiaoxia Fu and Nemanja Vaci
“Predicting Readiness To Engage In Psychotherapy From Self-Referral Forms: A Natural Language Processing Analysis Of Clinical Documents” Metadata:
- Title: ➤ Predicting Readiness To Engage In Psychotherapy From Self-Referral Forms: A Natural Language Processing Analysis Of Clinical Documents
- Authors: Xiaoxia FuNemanja Vaci
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- Internet Archive ID: osf-registrations-bk8xh-v1
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This study bridges clinical psychology and artificial intelligence to address a critical gap in mental health triage: predicting therapy engagement from free-text narratives in self-referral forms. Purpose: While readiness for therapy is traditionally assessed via structured questionnaires (e.g., URICA, RTQ), these miss nuanced insights in unstructured text. We develop a novel Natural Language Processing (NLP) framework grounded in the Transtheoretical Model (TTM) of change to: - Quantify linguistic markers of readiness from free-text responses ("What problems bring you here?" and "What do you hope to achieve?"). - Model how system responsiveness (referral-to-offer time) moderates the link between readiness and first-session attendance. - Build integrated prediction tools to optimize triage—prioritizing high-risk students (e.g., "hidden ready" cases). Methods: Using 250 anonymized student referrals from the University of Sheffield, we: - Annotate TTM stages (Precontemplation → Maintenance) with inter-rater reliability checks. - Convert text to semantic embeddings via sentence-transformers, training SVM classifiers to derive NLP readiness scores. - Test hypotheses through contingency analyses (H1: self-report vs. NLP concordance) and logistic regression (H2-H4: attendance prediction with time moderation). Expected Outcomes: - Validation of free-text as a clinically meaningful readiness indicator (complementing Item 9 self-reports). - Quantification of time-sensitive engagement: Shorter waits amplify linguistic readiness effects. - Actionable thresholds for triage systems: NLP flags for students needing accelerated scheduling or motivational support. Impact: This work pioneers the operationalization of TTM theory through NLP, enabling proactive resource allocation in overburdened mental health services.
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- Added Date: 2025-07-14 19:01:05
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