Background: Mental crises have high prevalences in adolescence. Early interventions appear to be highly important to diminish the risk of the deterioration, recurrence, or chronification of symptoms. In recent years, various providers have started offering live chat support in psychological crises. The messenger-based psychological counseling service krisenchat aims to support young people in crises and, if necessary, provide a recommendation for a referral to the health care system or to seek further help from a trusted adult person. Objective: This study aimed to investigate the impact of using the counseling service of krisenchat on the further help-seeking behavior of young people, and to identify associated factors of further help-seeking. Methods: This longitudinal study analyzed anonymous data from 247 individuals who used krisenchat between October 2021 and March 2022, and received a recommendation for further help-seeking. An online survey directly after the chat assessed the perceived helpfulness of the chat and well-being after the chat. After 4 weeks, further help-seeking, facilitators and barriers to help-seeking, and self-efficacy were assessed in an online follow-up survey. Results: The most frequently recommended services or persons to seek further help from included a psychotherapist or social psychiatric service (75/225, 33.3%), a school psychologist or school social worker (52/225, 23.1%), and the user’s parents (45/225, 20.0%). Of the 247 users, 120 (48.6%) indicated that they contacted the recommended service or person, and of these, 87 (72.5%) stated that they already had an appointment (or talk) with the respective service or person or that an appointment (or talk) was scheduled. The most frequently reported facilitators for further help-seeking were mental health literacy (54/120, 45.0%), improvement of self-efficacy (55/120, 45.8%), and symptom recognition (40/120, 33.3%). In users not displaying further help-seeking behavior, the most frequent barriers included stigmatization (60/127, 47.2%), lack of mental health literacy (59/127, 46.5%), need for self-reliance and autonomy (53/127, 41.7%), and negative family beliefs regarding help services (53/127, 41.7%). Subgroup comparisons indicated significantly higher levels of self-efficacy in users displaying further help-seeking behavior than in those not displaying further help-seeking behavior. Both subgroups did not differ in gender, age, recommended service or person, chat topics, perceived helpfulness, and well-being. Conclusions: The findings of this study indicate that children and young adults receiving counseling on krisenchat benefit in terms of seeking further help. Further help-seeking seems to be associated with higher levels of self-efficacy. Trial Registration: Deutsches Register Klinischer Studien DRKS00026671; https://tinyurl.com/4fm5xe68

Background: Suicide represents a critical public health concern, and machine learning (ML) models offer the potential for identifying at-risk individuals. Recent studies using benchmark datasets and real-world social media data have demonstrated the capability of pretrained large language models in predicting suicidal ideation and behaviors (SIB) in speech and text. Objective: This study aimed to (1) develop and implement ML methods for predicting SIBs in a real-world crisis helpline dataset, using transformer-based pretrained models as a foundation; (2) evaluate, cross-validate, and benchmark the model against traditional text classification approaches; and (3) train an explainable model to highlight relevant risk-associated features. Methods: We analyzed chat protocols from adolescents and young adults (aged 14-25 years) seeking assistance from a German crisis helpline. An ML model was developed using a transformer-based language model architecture with pretrained weights and long short-term memory layers. The model predicted suicidal ideation (SI) and advanced suicidal engagement (ASE), as indicated by composite Columbia-Suicide Severity Rating Scale scores. We compared model performance against a classical word-vector-based ML model. We subsequently computed discrimination, calibration, clinical utility, and explainability information using a Shapley Additive Explanations value-based post hoc estimation model. Results: The dataset comprised 1348 help-seeking encounters (1011 for training and 337 for testing). The transformer-based classifier achieved a macroaveraged area under the curve (AUC) receiver operating characteristic (ROC) of 0.89 (95% CI 0.81-0.91) and an overall accuracy of 0.79 (95% CI 0.73-0.99). This performance surpassed the word-vector-based baseline model (AUC-ROC=0.77, 95% CI 0.64-0.90; accuracy=0.61, 95% CI 0.61-0.80). The transformer model demonstrated excellent prediction for nonsuicidal sessions (AUC-ROC=0.96, 95% CI 0.96-0.99) and good prediction for SI and ASE, with AUC-ROCs of 0.85 (95% CI 0.97-0.86) and 0.87 (95% CI 0.81-0.88), respectively. The Brier Skill Score indicated a 44% improvement in classification performance over the baseline model. The Shapley Additive Explanations model identified language features predictive of SIBs, including self-reference, negation, expressions of low self-esteem, and absolutist language. Conclusions: Neural networks using large language model–based transfer learning can accurately identify SI and ASE. The post hoc explainer model revealed language features associated with SI and ASE. Such models may potentially support clinical decision-making in suicide prevention services. Future research should explore multimodal input features and temporal aspects of suicide risk.

Chat-based counseling hotlines emerged as a promising low-threshold intervention for youth mental health. However, despite the resulting availability of large text corpora, little work has investigated Natural Language Processing (NLP) applications within this setting. Therefore, this preregistered approach (OSF: XA4PN) utilizes a sample of approximately 19,000 children and young adults that received a chat consultation from a 24/7 crisis service in Germany. Around 800,000 messages were used to predict whether chatters would contact the service again, as this would allow the provision of or redirection to additional treatment. We trained an XGBoost Classifier on the words of the anonymized conversations, using repeated cross-validation and bayesian optimization for hyperparameter search. The best model was able to achieve an AUROC score of 0.68 (p < 0.01) on the previously unseen 3942 newest consultations. A shapely-based explainability approach revealed that words indicating younger age or female gender and terms related to self-harm and suicidal thoughts were associated with a higher chance of recontacting. We conclude that NLP-based predictions of recurrent contact are a promising path toward personalized care at chat hotlines.

Personality development is related to life events that change social roles and environments. Here, we provide an overview of the differences between personal and collective life events relevant to personality development. Following some basic assumptions about the malleability of personality traits due to life events, we discuss the differences in the thematic, social, spatial, and temporal characteristics of personal and collective life events. Personal life events often cover the domains of health, work, family, and love in individual people's lives, while collective life events refer to disasters and power struggles that affect many people. Collective life events are different because they can (a) trigger different personal life events for different people, (b) indirectly affect many more individuals who identify with a directly affected group, and (c) change social role demands through cultural changes. We discuss how these, and other differences affect the way researchers should investigate collective life events.

Background: Text messaging is widely used by young people for communicating and seeking mental health support through chat-based helplines. However, written communication lacks nonverbal cues, and language usage is an important source of information about a person’s mental health state and is known to be a marker for psychopathology. Objective: The aim of the study was to investigate language usage, and its gender differences and associations with the presence of psychiatric symptoms within a chat counseling service for adolescents and young adults. Methods: For this study, the anonymized chat content of a German messenger–based psychosocial chat counseling service for children and adolescents (“krisenchat”) between May 2020 and July 2021 was analyzed. In total, 661,131 messages from 6962 users were evaluated using Linguistic Inquiry and Word Count, considering the following linguistic variables: first-person singular and plural pronouns, negations, positive and negative emotion words, insight words, and causation words. Descriptive analyses were performed, and gender differences of those variables were evaluated. Finally, a binary logistic regression analysis examined the predictive value of linguistic variables on the presence of psychiatric symptoms. Results: Across all analyzed chats, first-person singular pronouns were used most frequently (965,542/8,328,309, 11.6%), followed by positive emotion words (408,087/8,328,309, 4.9%), insight words (341,460/8,328,309, 4.1%), negations (316,475/8,328,309, 3.8%), negative emotion words (266,505/8,328,309, 3.2%), causation words (241,520/8,328,309, 2.9%), and first-person plural pronouns (499,698/8,328,309, 0.6%). Female users and users identifying as diverse used significantly more first-person singular pronouns and insight words than male users (both P<.001). Negations were significantly more used by female users than male users or users identifying as diverse (P=.007). Similar findings were noted for negative emotion words (P=.01). The regression model of predicting psychiatric symptoms by linguistic variables was significant and indicated that increased use of first-person singular pronouns (odds ratio [OR] 1.05), negations (OR 1.11), and negative emotion words (OR 1.15) was positively associated with the presence of psychiatric symptoms, whereas increased use of first-person plural pronouns (OR 0.39) and causation words (OR 0.90) was negatively associated with the presence of psychiatric symptoms. Suicidality, self-harm, and depression showed the most significant correlations with linguistic variables. Conclusions: This study highlights the importance of examining linguistic features in chat counseling contexts. By integrating psycholinguistic findings into counseling practice, counselors may better understand users’ psychological processes and provide more targeted support. For instance, certain linguistic features, such as high use of first-person singular pronouns, negations, or negative emotion words, may indicate the presence of psychiatric symptoms, particularly among female users and users identifying as diverse. Further research is needed to provide an in-depth look into language processes within chat counseling services.

Background: Chat-based counseling services are popular for the low-threshold provision of mental health support to youth. In addition, they are particularly suitable for the utilization of natural language processing (NLP) for improved provision of care. Objective: Consequently, this paper evaluates the feasibility of such a use case, namely, the NLP-based automated evaluation of satisfaction with the chat interaction. This preregistered approach could be used for evaluation and quality control procedures, as it is particularly relevant for those services. Methods: The consultations of 2609 young chatters (around 140,000 messages) and corresponding feedback were used to train and evaluate classifiers to predict whether a chat was perceived as helpful or not. On the one hand, we trained a word vectorizer in combination with an extreme gradient boosting (XGBoost) classifier, applying cross-validation and extensive hyperparameter tuning. On the other hand, we trained several transformer-based models, comparing model types, preprocessing, and over- and undersampling techniques. For both model types, we selected the best-performing approach on the training set for a final performance evaluation on the 522 users in the final test set. Results: The fine-tuned XGBoost classifier achieved an area under the receiver operating characteristic score of 0.69 (P<.001), as well as a Matthews correlation coefficient of 0.25 on the previously unseen test set. The selected Longformer-based model did not outperform this baseline, scoring 0.68 (P=.69). A Shapley additive explanations explainability approach suggested that help seekers rating a consultation as helpful commonly expressed their satisfaction already within the conversation. In contrast, the rejection of offered exercises predicted perceived unhelpfulness. Conclusions: Chat conversations include relevant information regarding the perceived quality of an interaction that can be used by NLP-based prediction approaches. However, to determine if the moderate predictive performance translates into meaningful service improvements requires randomized trials. Further, our results highlight the relevance of contrasting pretrained models with simpler baselines to avoid the implementation of unnecessarily complex models. Trial Registration: Open Science Framework SR4Q9; https://osf.io/sr4q9