Luisa Orrù

Emergency management has become a widely discussed topic in recent years, particularly in relation to different types of emergencies such as climate change, natural disasters, public health, and armed conflicts. The Covid‐19 pandemic has triggered unprecedented uncertainty, sparking a global emergency that raised concerns not only about health but also social, economic, and environmental stability. At a time when psychology is re‐evaluating its role in supporting processes of change during emergencies, a qualitative approach can offer a concrete response to the needs that the population highlights. This study explores, over a period of more than a year (from April 2020 to May 2021), the discursive productions shared by the Italian population on social media in relation to the Covid‐19 pandemic emergency, in order to observe the impact on public health and identify clinical implications. A text analysis was performed using an annotation system based on 24 discursive modalities (learned with specific training by two humans and one machine learning model). IRaMuTeQ was also used for content detection and descending hierarchical classification. The analysis highlighted the need for support both at a clinical level and through sociopolitical intervention, based on how individuals expressed their experiences and concerns. Specifically, people's discourse tends to rigidly adhere to the status quo, where a focus on the “here and now” precludes the development of future‐oriented health goals. Also, by prioritizing institutional critique over personal accountability, individuals adopt the role of passive observers rather than proactive participants in managing their own and the community's well‐being. These results highlight how machine learning models can assist clinicians in better understanding the health needs of the population during emergencies, offering greater precision and speed in operational decision‐making.

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHIs). In particular, it explores how AI and NLP can facilitate human‐machine interaction in therapy by analysing how language is used within clinical conversations and providing personalized, real‐time interventions. Following PRISMA guidelines, a systematic review of literature from 2019 to 2025 identified 17 studies that met inclusion criteria, emphasizing AI's use in psychological assessment and intervention. The review focuses on two key aspects: the functions and applications of NLP‐based systems in clinical practice and the advantages and benefits they offer for both psychologists and patients. Findings suggest that NLP‐driven AI systems enhance both patient engagement and clinician efficiency, offering scalable, cost‐effective solutions that improve access and personalization. However, challenges remain, including ethical concerns around data privacy, lack of standardization, limited generalizability across disorders and reduced human empathy. Moreover, current systems are primarily designed for well‐defined conditions like anxiety and depression, with limited applicability to complex or comorbid psychological presentations. This review underscores the importance of supervised, ethically governed AI implementation. While AI holds substantial promise in augmenting clinical psychology, its success depends on maintaining human oversight, ensuring transparency and establishing shared scientific and ethical standards across the psychological community.

Artificial Intelligence (AI) technologies are rapidly evolving and their integration into psychological practices has progressively expanded, offering new tools for diagnosis, treatment and therapeutic monitoring. This review examines the transformative role of AI, particularly Natural Language Processing (NLP) systems, in reshaping clinical psychology and digital mental health interventions (DMHIs). In particular, it explores how AI and NLP can facilitate human-machine interaction in therapy by analysing how language is used within clinical conversations and providing personalized, real-time interventions. Following PRISMA guidelines, a systematic review of literature from 2019 to 2025 identified 17 studies that met inclusion criteria, emphasizing AI's use in psychological assessment and intervention. The review focuses on two key aspects: the functions and applications of NLP-based systems in clinical practice and the advantages and benefits they offer for both psychologists and patients. Findings suggest that NLP-driven AI systems enhance both patient engagement and clinician efficiency, offering scalable, cost-effective solutions that improve access and personalization. However, challenges remain, including ethical concerns around data privacy, lack of standardization, limited generalizability across disorders and reduced human empathy. Moreover, current systems are primarily designed for well-defined conditions like anxiety and depression, with limited applicability to complex or comorbid psychological presentations. This review underscores the importance of supervised, ethically governed AI implementation. While AI holds substantial promise in augmenting clinical psychology, its success depends on maintaining human oversight, ensuring transparency and establishing shared scientific and ethical standards across the psychological community.

Despite their diverse assumptions, clinical psychology approaches share the goal of mental health promotion. The literature highlights their usefulness, but also some issues related to their effectiveness, such as their difficulties in monitoring psychological change. The elective strategy for activating and managing psychological change is the clinical question. But how do different types of questions foster psychological change? This work tries to answer this issue by studying therapist–patient interactions with a ML model for text analysis. The goal was to investigate how psychological change occurs thanks to different types of questions, and to see if the ML model recognized this difference in analyzing patients’ answers to therapists’ clinical questions. The experimental dataset of 14,567 texts was divided based on two different question purposes, splitting answers in two categories: those elicited by questions asking patients to start describing their clinical situation, or those from asking them to detail how they evaluate their situation and mental health condition. The hypothesis that these categories are distinguishable by the model was confirmed by the results, which corroborate the different valences of the questions. These results foreshadow the possibility to train ML and AI models to suggest clinical questions to therapists based on patients’ answers, allowing the increase of clinicians’ knowledge, techniques, and skills.

The climate change emergency is one of the most important challenges of our time, and its impact on mental health has been evident for years. It is very important for clinicians to delve deeper into these manifestations. There are a wide variety of constructs, symptoms, and scales to measure the impact of climate change on mental health. Eco-anxiety is one of the constructs that has specifically emerged, in association with worry, about the future. In mental health studies, it is important to explore the relationship between eco-anxiety and emotion regulation and how much this relationship leads to worry about the future in order to be able to offer clinical intervention recommendations. Indeed, the hypothesis of this study is that it is possible to develop worries about the future when symptoms of eco-anxiety and a poor ability to regulate emotions are present. Particularly, emotion dysregulation could increase eco-anxiety symptoms and generate worries about one’s own future. For this reason, we have chosen to explore the relationships between these three constructs with the use of a mediation analysis. For this research, 351 participants were recruited in Italy. The proposed mediation model highlighted the findings that emotion dysregulation was positively related with eco-anxiety and that eco-anxiety predicts worry about the future. An association between emotion dysregulation and worry about the future was present. Eco-anxiety appeared to be an important mediator between emotional dysregulation and worry about the future. Emotion regulation could play a pivotal role in addressing concerns about the future. These findings could pave the way for exploring new research avenues and potential clinical interventions.

Literature about global emergencies and their impact on people’s health underlines the need to improve the social cohesion of human community and the availability of tools to support people and foster community interactions. This paper illustrates research aimed at describing and measuring human interactions in the Veneto community and its changing during the COVID-19 pandemic. 50,000 text occurrences from social media and newspapers about these topics were analyzed between December 2021 and January 2022. People present themselves as members of different teams, pursuing conflicting aims, and attributing the decision-making responsibility of emergencies management exclusively to governments, without considering themselves as active parts of the community. This delegation process on citizens’ behalf can affect their health: by taking minor responsibility in handling the repercussions of these events on the community and by arguing over the most effective way to deal with them, they risk freezing and waiting for action by third parties, thus leaving mutual interactions and the promotion of their own health at a standstill. Local institutions can use these data to shape prevention policies to manage the community’s emergencies and use them as opportunities to promote public health.