Hasil untuk "Therapeutics. Psychotherapy"

Venessya Manuhutu, Sri Aryanti Kristianingsih, Enjang Wahyuningrum

Adolescents are a vulnerable group to sexual violence due to a lack of sexual education, media influence, and complex social dynamics that exacerbate their vulnerability to sexual violence. This vulnerability is reflected in data on sexual violence in Indonesia, with 16,781 cases of sexual violence in 2024. Sexual violence is not only perpetrated by strangers but can also be perpetrated by parents or family members. This study aims to describe and understand the process of self-acceptance in adolescent girls who are victims of familial sexual abuse. Data were collected through observation and in-depth interviews with two participants who experienced familial sexual abuse at the ages of 14 and 13. The results showed that the participants had self-acceptance, reflected in the awareness that they still have support, a greater appreciation for life, and the choice to continue living. The process of self-acceptance can occur in stages, namely through resisting, exploring, tolerating, allowing, and befriending. This process is shaped by internal and external factors that have the potential to increase self-acceptance.

Jiatao Quan, Ziyue Li, Tian Qi Zhu et al.

As large language models (LLMs) are embedded into mental health technologies, they are often framed either as tools assisting therapists or autonomous therapeutic systems. Such perspectives overlook their potential to mediate relational complexities in therapy, particularly for systemically marginalized clients. Drawing on in-depth interviews with 12 therapists and 12 marginalized clients in China, including LGBTQ+ individuals or those from other marginalized backgrounds, we identify enduring relational challenges: difficulties building trust amid institutional barriers, the burden clients carry in educating therapists about marginalized identities, and challenges sustaining authentic self-disclosure across therapy and daily life. We argue that addressing these challenges requires AI systems capable of actively mediating underlying knowledge gaps, power asymmetries, and contextual disconnects. To this end, we propose the Dynamic Boundary Mediation Framework, which reconceptualizes LLM-enhanced systems as adaptive boundary objects that shift mediating roles across therapeutic stages. The framework delineates three forms of mediation: Epistemic (reducing knowledge asymmetries), Relational (rebalancing power dynamics), and Contextual (bridging therapy-life discontinuities). This framework offers a pathway toward designing relationally accountable AI systems that center the lived realities of marginalized users and more effectively support therapeutic relationships.

Yi Feng, Jiaqi Wang, Wenxuan Zhang et al.

Recent progress in large language models (LLMs) has opened new possibilities for mental health support, yet current approaches lack realism in simulating specialized psychotherapy and fail to capture therapeutic progression over time. Narrative therapy, which helps individuals transform problematic life stories into empowering alternatives, remains underutilized due to limited access and social stigma. We address these limitations through a comprehensive framework with two core components. First, INT (Interactive Narrative Therapist) simulates expert narrative therapists by planning therapeutic stages, guiding reflection levels, and generating contextually appropriate expert-like responses. Second, IMA (Innovative Moment Assessment) provides a therapy-centric evaluation method that quantifies effectiveness by tracking "Innovative Moments" (IMs), critical narrative shifts in client speech signaling therapy progress. Experimental results on 260 simulated clients and 230 human participants reveal that INT consistently outperforms standard LLMs in therapeutic quality and depth. We further demonstrate the effectiveness of INT in synthesizing high-quality support conversations to facilitate social applications.

Fabian Schmidt, Karin Hammerfald, Henrik Haaland Jahren et al.

Common factors and microcounseling skills are critical to the effectiveness of psychotherapy. Understanding and measuring these elements provides valuable insights into therapeutic processes and outcomes. However, automatic identification of these change principles from textual data remains challenging due to the nuanced and context-dependent nature of therapeutic dialogue. This paper introduces CFiCS, a hierarchical classification framework integrating graph machine learning with pretrained contextual embeddings. We represent common factors, intervention concepts, and microcounseling skills as a heterogeneous graph, where textual information from ClinicalBERT enriches each node. This structure captures both the hierarchical relationships (e.g., skill-level nodes linking to broad factors) and the semantic properties of therapeutic concepts. By leveraging graph neural networks, CFiCS learns inductive node embeddings that generalize to unseen text samples lacking explicit connections. Our results demonstrate that integrating ClinicalBERT node features and graph structure significantly improves classification performance, especially in fine-grained skill prediction. CFiCS achieves substantial gains in both micro and macro F1 scores across all tasks compared to baselines, including random forests, BERT-based multi-task models, and graph-based methods.

Subin Kim, Hoonrae Kim, Jihyun Lee et al.

Recent studies have explored the use of large language models (LLMs) in psychotherapy; however, text-based cognitive behavioral therapy (CBT) models often struggle with client resistance, which can weaken therapeutic alliance. To address this, we propose a multimodal approach that incorporates nonverbal cues, which allows the AI therapist to better align its responses with the client's negative emotional state. Specifically, we introduce a new synthetic dataset, Mirror (Multimodal Interactive Rolling with Resistance), which is a novel synthetic dataset that pairs each client's statements with corresponding facial images. Using this dataset, we train baseline vision language models (VLMs) so that they can analyze facial cues, infer emotions, and generate empathetic responses to effectively manage client resistance. These models are then evaluated in terms of both their counseling skills as a therapist, and the strength of therapeutic alliance in the presence of client resistance. Our results demonstrate that Mirror significantly enhances the AI therapist's ability to handle resistance, which outperforms existing text-based CBT approaches. Human expert evaluations further confirm the effectiveness of our approach in managing client resistance and fostering therapeutic alliance.

Muhammad Syukron Zun Nurain, Nur Fitriyana, Azrianti Ishamiyah

Abstrak Penelitian ini dilatarbelakangi seruan Al-Tayyeb sebagai Syekh al-Azhar yang bertanggungjawab menghentikan menggunakan agama dan sekte untuk menghasut kebencian, kekerasan, fanatisme buta, menggunakan nama Tuhan untuk membenarkan tindakan pembunuhan, pengungsian, terorisme, dan penindasan. Ia menyerukan kepada pemimpin seluruh dunia untuk segera melakukan intervensi guna mengakhiri perang dan konflik yang membawa manusia kembali ke masa kemunduran peradaban di era digital. Oleh karena itu penelitian ini penting untuk memahami keunggulan moderasi beragama dalam paham spiritualitas Syekh Al-Tayyeb. Jenis penelitian ini penelitian kualitatif dengan analisis deskriptif dan kritis. Objek formalnya pemikiran Syekh Al-Tayyeb pada pidato kegiatan Konferensi Persaudaraan Manusia di UEA 4 Febuari 2019 kanal YouTube Dubai Media. Objek materialnya adalah karya yang berhubungan dengan moderasi beragama. Penelitian ini menggunakan pendekatan fenomenologi agama dengan menggunakan teori moderasi beragama Kementerian Agama RI dan teori spiritualitas. Analisis nilai-nilai moderasi beragama Syekh Al-Tayyeb menggunakan analisis wacana. Hasil penelitian ini membuktikan moderasi beragama Syekh Al-Tayyeb berada dalam paham spiritualitas interaktif dengan empat indikator moderasi yang berada pada pilar pemikiran, pilar gerakan dan pilar tradisi keagamaan. Prinsip-prinsip dasar moderasi beragamanya tertuang dalam 12 butir dokumen Abu Dhabi. 

Miray Özcan, Ayşenur Aktaş

Alanyazındaki Ortoreksiya Nervoza (ON) etiyolojisi üzerinde yürütülen araştırmalar incelendiğinde nedenselliğine dair uzlaşılamayan sonuçların var olduğu gözlenmektedir. Bu sebeple, bu çalışmada ON etiyolojisinin anlaşılmasına dair yeme tutumları, obsesif inançlar ve üstbiliş değişkenlerinin ON eğilimi üzerindeki yordayıcı etkileri incelenmek istenmektedir. Alanyazında, üniversite öğrencisi olmak olumsuz yeme davranışları ve yeme bozuklukları geliştirmede risk faktörü olarak ele alındığından ve aynı zamanda bu grup sağlıklı yeme ile ilgili popüler kültüre de daha fazla maruz kaldığından araştırmanın örneklemi 437 üniversite öğrencisinden (Ort.yaş = 22.7) oluşmaktadır. Çalışmada Demografik Bilgi Formunun yanında Ortoreksiya Nervoza Ölçeği (ORTO-11), Obsesif İnançlar ölçeği (OİÖ-44), Yeme Tutum Testi (YTT-26) ve Üstbiliş Ölçeği (ÜBÖ-30) kullanılmıştır. Verileri sınamak amacıyla Pearson momentler çarpımı korelasyon analizi, ANOVA, t-test ve basit doğrusal regresyon analizleri uygulanmıştır. ON eğilimini ölçen ORTO-11’den alınan yüksek puanların düşük ON eğilimine işaret ettiğini de göz önünde bulundurarak analiz sonuçlarını ele aldığımızda obsesif inançlar, yeme tutumları ve üstbiliş bağımsız değişkenleri ile ON eğilimi arasında negatif yönde anlamlı ilişkiler olduğu görülmüştür. Böylece, Obsesif İnançlar Ölçeği toplam puanı ve alt boyutları (sorumluluk/tehlike beklentisi, mükemmeliyetçilik/kesinlik), Yeme Tutum Testi toplam puanı ve alt boyutları (yeme meşguliyeti, kısıtlama), üstbiliş toplam puanı ve alt boyutlarının (olumlu inançlar, bilişsel farkındalık, düşünceleri kontrol ihtiyacı) ON eğilimi üzerindeki yordayıcı etkileri gözlenmiştir. Araştırmadan elde edilen sonuçlar alanyazındaki benzer çalışmaların bulguları ile karşılaştırılarak tartışılmış ve bu çalışmanın, ileride ON eğiliminin etiyolojisi üzerine yapılacak araştırmalara ışık tutması amaçlanmıştır.

Giorgos Lysandrou, Roma English Owen, Vanja Popovic et al.

There exists an invisible barrier between healthcare professionals' perception of a patient's clinical experience and the reality. This barrier may be induced by the environment that hinders patients from sharing their experiences openly with healthcare professionals. As patients are observed to discuss and exchange knowledge more candidly on social media, valuable insights can be leveraged from these platforms. However, the abundance of non-patient posts on social media necessitates filtering out such irrelevant content to distinguish the genuine voices of patients, a task we refer to as patient voice classification. In this study, we analyse the importance of linguistic characteristics in accurately classifying patient voices. Our findings underscore the essential role of linguistic and statistical text similarity analysis in identifying common patterns among patient groups. These results allude to even starker differences in the way patients express themselves at a disease level and across various therapeutic domains. Additionally, we fine-tuned a pre-trained Language Model on the combined datasets with similar linguistic patterns, resulting in a highly accurate automatic patient voice classification. Being the pioneering study on the topic, our focus on extracting authentic patient experiences from social media stands as a crucial step towards advancing healthcare standards and fostering a patient-centric approach.

Gerhard Hilgers, Miriam Schwarze, Hans Rabus

At the Heidelberg Ion-Beam Therapy Center, the track structure of carbon ions of therapeutic energy after penetrating layers of simulated tissue was investigated for the first time. Measurements were conducted with carbon ion beams of different energies and polymethyl methacrylate (PMMA) absorbers of different thicknesses to realize different depths in the phantom along the pristine Bragg peak. Ionization cluster size (ICS) distributions resulting from the mixed radiation field behind the PMMA absorbers were measured using an ion-counting nanodosimeter. Two different measurements were carried out: (i) variation of the PMMA absorber thickness with constant carbon ion beam energy and (ii) combined variation of PMMA absorber thickness and carbon ion beam energy such that the kinetic energy of the carbon ions in the target volume is constant. The data analysis revealed unexpectedly high mean ICS values compared to stopping power calculations and the data measured at lower energies in earlier work. This suggests that in the measurements the carbon ion kinetic energies behind the PMMA absorber may have deviated considerably from the expected values obtained by the calculations. In addition, the results indicate the presence of a marked contribution of nuclear fragments to the measured ICS distributions, especially if the carbon ion does not cross the target volume.

Daniil Filienko, Yinzhou Wang, Caroline El Jazmi et al.

While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy (PST) session via text, particularly during the symptom identification and assessment phase for personalized goal setting. We present evaluation results of the models' performances by automatic metrics and experienced medical professionals. We demonstrate that the models' capability to deliver protocolized therapy can be improved with the proper use of prompt engineering methods, albeit with limitations. To our knowledge, this study is among the first to assess the effects of various prompting techniques in enhancing a generalist model's ability to deliver psychotherapy, focusing on overall quality, consistency, and empathy. Exploring LLMs' potential in delivering psychotherapy holds promise with the current shortage of mental health professionals amid significant needs, enhancing the potential utility of AI-based and AI-enhanced care services.

Jiashuo Cao, Wujie Gao, Yun Suen Pai et al.

The advent of technology-enhanced interventions has significantly transformed mental health services, offering new opportunities for delivering psychotherapy, particularly in remote settings. This paper reports on a pilot study exploring the use of Virtual Reality (VR) as a medium for remote counselling. The study involved four experienced psychotherapists who evaluated three different virtual environments designed to support remote counselling. Through thematic analysis of interviews and feedback, we identified key factors that could be critical for designing effective virtual environments for counselling. These include the creation of clear boundaries, customization to meet specific therapeutic needs, and the importance of aligning the environment with various therapeutic approaches. Our findings suggest that VR can enhance the sense of presence and engagement in remote therapy, potentially improving the therapeutic relationship. In the paper we also outline areas for future research based on these pilot study results.

Miriam Schwarze, Gerhard Hilgers, Hans Rabus

Previously reported nanodosimetric measurements of therapeutic-energy carbon ions penetrating simulated tissue have produced results that are incompatible with the predicted mean energy of the carbon ions in the nanodosimeter and previous experiments with lower energy monoenergetic beams. The purpose of this study is to explore the origin of these discrepancies. Detailed simulations using the Geant4 toolkit were performed to investigate the radiation field in the nanodosimeter and provide input data for track structure simulations, which were performed with a developed version of the PTra code. The Geant4 simulations show that with the narrow-beam geometry employed in the experiment, only a small fraction of the carbon ions traverse the nanodosimeter and their mean energy is between 12 % and 30 % lower than the targeted values. Only about one-third or less of these carbon ions hit the trigger detector. The track structure simulations indicate that the observed enhanced ionization cluster sizes are mainly due to coincidences with events in which carbon ions miss the trigger detector. In addition, the discrepancies observed for high absorber thicknesses of carbon ions traversing the target volume could be explained by assuming an increase in thickness or interaction cross-sections in the order of 1 %. The results show that even with strong collimation of the radiation field, future nanodosimetric measurements of clinical carbon ion beams will require large trigger detectors to register all events with carbon ions traversing the nanodosimeter. Energy loss calculations of the primary beam in the absorbers are insufficient and should be replaced by detailed simulations when planning such experiments. Uncertainties of the interaction cross-sections in simulation codes may shift the Bragg peak position.

Alexander Vanin, Vadim Bolshev, Anastasia Panfilova

This study explores the use of Large language models to analyze therapist remarks in a psychotherapeutic setting. The paper focuses on the application of BERTopic, a machine learning-based topic modeling tool, to the dialogue of two different groups of therapists (classical and modern), which makes it possible to identify and describe a set of topics that consistently emerge across these groups. The paper describes in detail the chosen algorithm for BERTopic, which included creating a vector space from a corpus of therapist remarks, reducing its dimensionality, clustering the space, and creating and optimizing topic representation. Along with the automatic topical modeling by the BERTopic, the research involved an expert assessment of the findings and manual topic structure optimization. The topic modeling results highlighted the most common and stable topics in therapists speech, offering insights into how language patterns in therapy develop and remain stable across different therapeutic styles. This work contributes to the growing field of machine learning in psychotherapy by demonstrating the potential of automated methods to improve both the practice and training of therapists. The study highlights the value of topic modeling as a tool for gaining a deeper understanding of therapeutic dialogue and offers new opportunities for improving therapeutic effectiveness and clinical supervision.

Inhwa Song, Sachin R. Pendse, Neha Kumar et al.

People experiencing severe distress increasingly use Large Language Model (LLM) chatbots as mental health support tools. Discussions on social media have described how engagements were lifesaving for some, but evidence suggests that general-purpose LLM chatbots also have notable risks that could endanger the welfare of users if not designed responsibly. In this study, we investigate the lived experiences of people who have used LLM chatbots for mental health support. We build on interviews with 21 individuals from globally diverse backgrounds to analyze how users create unique support roles for their chatbots, fill in gaps in everyday care, and navigate associated cultural limitations when seeking support from chatbots. We ground our analysis in psychotherapy literature around effective support, and introduce the concept of therapeutic alignment, or aligning AI with therapeutic values for mental health contexts. Our study offers recommendations for how designers can approach the ethical and effective use of LLM chatbots and other AI mental health support tools in mental health care.

Anmol Goel, Nico Daheim, Christian Montag et al.

Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language model-based solutions. Such reframing is typically non-trivial and requires multiple rationalization steps to uncover the underlying issue of a negative thought and transform it to be more positive. However, this rationalization process is currently neglected by both datasets and models which reframe thoughts in one step. In this work, we address this gap by augmenting open-source datasets for positive text rewriting with synthetically-generated Socratic rationales using a novel framework called \textsc{SocraticReframe}. SocraticReframe uses a sequence of question-answer pairs to rationalize the thought rewriting process. We show that such Socratic rationales significantly improve positive text rewriting for different open-source LLMs according to both automatic and human evaluations guided by criteria from psychotherapy research. We validate our framework and the synthetic rationalizations with expert judgements from domain experts and psychology students in an IRB-approved annotation study. Our findings highlight the potential of utilizing the synergy between LLM reasoning and established psychotherapy techniques to build assistive solutions for reframing negative thoughts.

I Made Sonny Gunawan, Hariadi Ahmad, Aluh Hartati

The aim of this research is to confirm the effectiveness of behavioristic counseling with modeling techniques in minimizing cyberbullying behavior carried out by students. This research uses a pretest-posttest control group design experimental approach. The subjects in this research were 8 students who attended State Senior High School 3 (SMA Negeri 3) Mataram. Data was collected using a Personality Scale in the form of a questionnaire developed based on indicators of cyberbullying behavior. Data analysis uses one-way Anova statistics. The results of this research reveal that behavioristic counseling using modeling techniques is effective in minimizing cyberbullying behavior. The forms of cyberbullying behavior that can be minimized well in this research are verbal cyberbullying behavior such as insulting behavior using bad words, and spreading rumors to embarrass one's friends.

Şükriye Açar, Mujgan Inozu

Obsessive distrust is conceptualized as an additional domain of relationship and partner-oriented obsessive-compulsive symptoms. The aim of this study was to adapt the Obsessive Distrust In-ventory designed to assess symptoms of obsessive distrust into Turkish and investigate its psychometric properties. The study sample consisted of 420 (227 females and 193 males) partici-pants aged between 18-59 and stated that they were in an ongoing romantic relationship. Partici-pants were asked to fill the scale set containing the Obsessive Distrust Inventory, Obsessive Compulsive Inventory-Revised Form, Obsessive Beliefs Questionnaire-9, Relationship Obses-sions and Compulsion Inventory, and Partner-Related Obsessive-Compulsive Symptoms Inven-tory via the internet. According to the results of the confirmatory factor analysis, the factor structure of the Obsessive Distrust Inventory was found to be compatible with the one-factor structure of the original scale. Other analyzes showed that the scale had a satisfactory level of convergent and discriminant validity. At the same time, the internal consistency coefficient, the split-half test correlations, and the Spearman-Brown coefficient showed that the scale had reliability values consistent with those of the original scale. This study demonstrated that the Obsessive Distrust Inventory meets the requirements of a valid and reliable measurement instrument and is suitable for use in the Turkish sample, allowing for cross-cultural comparison.

Sonish Sivarajkumar, Pratyush Tandale, Ankit Bhardwaj et al.

Transcription factors (TFs) play a vital role in the regulation of gene expression thereby making them critical to many cellular processes. In this study, we used graph machine learning methods to create a compendium of TF cascades using data extracted from the STRING database. A TF cascade is a sequence of TFs that regulate each other, forming a directed path in the TF network. We constructed a knowledge graph of 81,488 unique TF cascades, with the longest cascade consisting of 62 TFs. Our results highlight the complex and intricate nature of TF interactions, where multiple TFs work together to regulate gene expression. We also identified 10 TFs with the highest regulatory influence based on centrality measurements, providing valuable information for researchers interested in studying specific TFs. Furthermore, our pathway enrichment analysis revealed significant enrichment of various pathways and functional categories, including those involved in cancer and other diseases, as well as those involved in development, differentiation, and cell signaling. The enriched pathways identified in this study may have potential as targets for therapeutic intervention in diseases associated with dysregulation of transcription factors. We have released the dataset, knowledge graph, and graphML methods for the TF cascades, and created a website to display the results, which can be accessed by researchers interested in using this dataset. Our study provides a valuable resource for understanding the complex network of interactions between TFs and their regulatory roles in cellular processes.

Cathy Richardson/Kinewesquao

At a recent conference in Lyon, an American researcher declared that we are all traumatised, all of us including counsellors, clients, workers, lawyers, activists. I was taken aback thinking, “wait a moment!” I am not willing to believe that, in the face of struggle and adversity, we are all mentally ill. Counsellors/therapists form part of a community circle responding to violence, harm, betrayal, grief and heartbreak. We are often inspired by our clients, their survivance, their resistance and the ways they signal injustice. The academic presenter was likely influenced by theories of compassion fatigue and vicarious trauma - ideas that accuse our clients of hurting us. I wonder to what extent these theories hurt us or get us thinking in ways that are individualising and unhelpful. 

Natanael Ananda Putra, Christiana Hari Soetjiningsih

This research aims to determine the relationship between self-efficacy and academic procrastination in psychology students. The method used is quantitative with a correlational design. 165 students aged 18 to 25 years studying for a bachelor's degree at the Faculty of Psychology at Satya Wacana Christian University were research participants using the Accidental Sampling technique. Research measurements used the General Self-Efficacy Scale (GSES) developed by Schwarzer and Jerusalem and the Academic Procrastination Scale (APS) developed by McCloskey, with data analysis using product moment correlation from Karl Pearson. The research found a negative relationship between self-efficacy and academic procrastination in Satya Wacana Christian University psychology students. This is indicated by a correlation coefficient of -0.147 with a significance value of 0.030 (p<0.05). This means that the higher the level of self-efficacy in students, the lower the level of procrastination in students, and conversely, the lower the level of self-efficacy in students, the higher the academic procrastination behaviour in students. The results of this research can be used as material to make efforts to increase self-efficacy to prevent procrastination behaviour in students.