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Detecting Patterns of Intimate Partner Violence Using Qualitative Analyses and Machine Learning Algorithms
Analyses 400 posts from women on intimate-partner-violence online forums using qualitative content analysis plus supervised text classification and unsupervised topic modelling. Classifies IPV subtypes and surfaces contextual patterns less visible in manual coding.
Large language models for psychosocial risk assessment: A multi-method evaluation across suicide, intimate partner violence, and substance misuse
A peer-reviewed, three-study evaluation of GPT-4 and Claude on detecting suicidality, intimate partner violence, and substance misuse from lived-experience vignettes, including a supervised multi-agent risk-assessment chatbot. Reports accuracy and severity alignment across the three risk domains.
Are Natural Language Processing Tools Ready for Predicting Violence Toward Self or Others?
PRISMA systematic review of 21 eligible studies applying natural language processing to clinical text for predicting violence toward self or others. Assesses predictive performance and methodological quality across the evidence base.
VERA-MH: Reliability and Validity of an Open-Source AI Safety Evaluation in Mental Health
An open-source, clinically grounded automated evaluation of chatbot safety in mental-health contexts, with an initial focus on suicide risk. It uses language-model user simulators and an LLM judge scoring five safety dimensions, validated against licensed-clinician ratings.
Delusional Experiences Emerging From AI Chatbot Interactions or "AI Psychosis"
A peer-reviewed psychiatric commentary in JMIR Mental Health analyzing delusional experiences that emerge from AI chatbot use, sometimes termed 'AI psychosis.' It argues psychiatry must reconsider the boundaries between environment, cognition, and technology.
Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers
Evaluates whether large language models can safely replace mental health providers by testing five therapy chatbots against clinical best-practice guidelines. Finds that models exhibit stigmatizing responses toward certain mental health conditions and give inappropriate or unsafe responses in scenarios involving delusions and suicidal ideation at substantially higher rates than human therapists.
Evaluating Large Language Models in Crisis Detection: A Real-World Benchmark from Psychological Support Hotlines (PsyCrisisBench)
PsyCrisisBench is a real-world crisis-detection benchmark built from 540 annotated transcripts from a psychological support hotline in Hangzhou, China. It evaluates 64 models on mood recognition, suicidal-ideation detection, plan identification, and risk evaluation.
Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study
Collects 294 Reddit posts from women self-identifying as experiencing intimate partner violence, defines eight information-need classes (shelters, legal, police, safety planning, etc.), augments to 2,216 samples with GPT-3.5, and fine-tunes GPT-3.5 for multiclass classification with a per-class training strategy. Reports an F1 of 70.5% on real posts.
Evaluating Reasoning LLMs for Suicide Screening with the Columbia-Suicide Severity Rating Scale
Tests six LLMs on classifying posts across the Columbia-Suicide Severity Rating Scale (C-SSRS) 7-point severity ladder, comparing model outputs with human annotations. Assesses automated suicide-risk screening and characterises misclassification patterns.
A machine learning approach to identifying suicide risk among text-based crisis counseling encounters
Develops a transformer-based model on 5,992 SafeUT crisis-counseling encounters to detect conversation-level suicide risk, benchmarked against a tf-idf baseline. Reports strong discrimination and better sensitivity on higher-risk cases despite noisy human counsellor labels, and positions the model as decision support.
A Systematic Literature Review of the Use of Computational Text Analysis Methods in Intimate Partner Violence Research
PRISMA systematic review across eight databases of 22 studies applying computational text-analysis and NLP methods to intimate-partner-violence research, spanning rule-based, classical machine-learning, deep-learning, and topic-modelling approaches. Data sources were predominantly social-media text, plus police, health/social-care, and litigation texts.
TRAP-18 indicators validated through the forensic linguistic analysis of targeted violence manifestos
Analyses 30 written and spoken manifestos authored by lone offenders who planned or committed targeted attacks (1974-2021), testing whether the behavior-based TRAP-18 threat-assessment instrument can be coded from language evidence alone. Finds 17 of 18 indicators codable from text.
Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting
Applies seven machine-learning algorithms with four word-list dictionaries (UMLS mental-health terms, DSM-IV diagnoses, a sentiment lexicon, and corpus frequencies) to de-identified forensic-inpatient clinical notes, predicting clinician-assigned risk ratings on three structured instruments: the HCR-20, START, and DASA. Reports best accuracy on the DASA dataset and flags that predicting actual endpoints (self-harm, harm-to-others, victimisation) needs further work.
Delivering Cognitive Behavior Therapy to Young Adults With Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial
Two-week unblinded randomized controlled trial (n=70, ages 18-28) comparing a CBT-delivering conversational agent (Woebot) with an information-only control. Measures change in depression and anxiety symptoms and engagement.