Search Results (6)

Psychedelic-assisted therapies (PATs) can produce rapid and sustained antidepressant effects, yet variability in response remains substantial. Identifying predictors and moderators is essential for optimising patient selection, preparation, and delivery. To map and synthesise the evidence on the predictors of antidepressant response to classic/serotonergic psychedelics administered with psychotherapeutic support in adults with depressive disorders, including treatment-resistant depression. Following PRISMA-ScR principles, we conducted a scoping review of major biomedical and psychology databases (PubMed (MEDLINE), Embase, PsycINFO, and Web of Science) and trial registries (searches September–October 2025), supplemented by reference-list screening. We included randomised trials, open-label studies, and naturalistic cohorts reporting associations between candidate predictors (baseline traits/clinical features, set/setting variables, acute in-session phenomenology, and biological measures) and validated depression outcomes. We charted study characteristics, analytic approaches (including moderation/mediation where available), and indicators of robustness (e.g., adjustment for overall intensity, preregistration, external validation). A total of 48 studies were included in the review. Across study designs, process-level features during the dosing session were the most consistent correlates of antidepressant improvement. Greater emotional breakthrough, mystical/unitive experiences, and ego dissolution-linked reappraisal/insight generally predicted larger and more durable symptom reductions, whereas anxiety-dominant or dysphoric states tended to attenuate benefit, often independent of overall subjective intensity. Set and setting—particularly a stronger therapeutic alliance and music experienced as resonant—predicted both the emergence of therapeutically salient acute experiences and downstream clinical gains. Baseline moderators showed smaller and mixed effects: PTSD comorbidity sometimes weakened trajectories; extensive prior psychedelic exposure was associated with smaller incremental gains; demographics were typically uninformative. Converging biological findings associated better outcomes with markers consistent with increased neural flexibility and plasticity (e.g., less segregated network dynamics; EEG indices), alongside peripheral changes implicating neurotrophic, inflammatory, and HPA axis pathways. Current evidence suggests that antidepressant response in PATs is driven less by static patient characteristics and more by what occurs during dosing and how the context shapes that experience. Optimising preparation, alliance, and music; facilitating emotional breakthrough and meaning making; and mitigating anxious dysregulation are actionable levers. Future trials should harmonise measures, pre-specify and validate moderators/mediators, intensively sample in-session experience and physiology, and report benefits and harms more consistently. Full article

This article discusses the development and preliminary validation of a self-report inventory of the patient’s perception of, and affective reaction to, their therapist during a psychotherapy session. First, we wrote a pool of 131 items, reviewed them based on subject matter experts’ review, and then collected validation data from a clinical sample of adult patients in individual therapy (N = 701). We used exploratory factor analysis and item response theory graded response models to select items, confirmatory factor analysis (CFA) to test the factor structure, and k-fold cross-validation to verify model robustness. Multi-group CFA examined measurement invariance across patients with different diagnoses (unipolar depression, bipolar disorder, and neither of these). Three factors produced short scales retaining the strongest items. The in-Session Patient Affective Reactions Questionnaire (SPARQ) has a two-factor structure, yielding a four-item Negative affect scale and a four-item Positive affect scale. The Relationship In-Session Questionnaire (RISQ) is composed of four items from the third factor with dichotomized responses. Both scales showed excellent psychometric properties and evidence of metric invariance across the three diagnostic groups: unipolar depression, bipolar disorder, and neither of these. The SPARQ and the RISQ scale can be used in clinical or research settings, with particular value for capturing the patient’s perspectives about their therapist and session-level emotional processes. Full article

This paper proposes a new methodology to improve territorial cohesion in clustering processes where many variables from different topics are considered. Clustering techniques provide added value to identify typologies, but there are still unsolved challenges when data contain an unbalanced number of variables from different topics. The territorial feature selection method (TFSM) is presented as a method to select the representative variable of each topic such that the interpretability of resulting clusters is preserved and the geographical cohesion is improved with respect to classical approaches. This paper also introduces the thermometer as a new knowledge acquisition tool that allows experts to transfer semantics to the data mining process. TFSM proposes the index of potential explainability ($E_k$) as the criteria to select the most promising variables for clustering. $E_k$ is based on the combination of inferential testing and metrics such as support. The proposal is applied with the INSESS-COVID19 database, where territorial groups of vulnerable populations were found. A set of 195 variables with 21 unbalanced thematic blocks is used to compare the results with a traditional multiview clustering analysis with promising results from both the geographical and the thematic point of view and the capacity to support further decision making. Full article

Interest in studying Massive Online Open Courses (MOOC) learners’ sessions has grown as a result of the retention and completion issues that these courses present. Applying process mining to study this phenomenon is difficult due to the freedom of navigation that these courses give their students. The goal of this research is to provide a domain-driven top-down method that enables educators who are unfamiliar with data and process analytics to search for a set of preset high-level concepts in their own MOOC data, hence simplifying the use of typical process mining techniques. This is accomplished by defining a three-stage process that generates a low-level event log from a minimum data model and then abstracts it to a high-level event log with seven possible learning dynamics that a student may perform in a session. By examining the actions of students who successfully completed a Coursera introductory programming course, the framework was tested. As a consequence, patterns in the repetition of content and assessments were described; it was discovered that students’ willingness to evaluate themselves increases as they advance through the course; and four distinct session types were characterized via clustering. This study shows the potential of employing event abstraction strategies to gain relevant insights from educational data. Full article

The purpose of this article is to narratively review the empirical literature on clinicians’ emotional, cognitive, and behavioral responses (i.e., countertransference) to depressive and other symptoms of patients with mood disorders. Therapist subjective responses (countertransference) can negatively affect both diagnostic and therapeutic processes, especially when they are not recognized and managed promptly. However, at the same time, countertransference recognition, processing, and management can help inform the diagnostic process and improve the therapy process and outcome. In the last couple of decades, the number of studies that empirically explore countertransference toward mood disordered patients, as well as its relationship with various characteristics of both patients and treatment, has increased. Current evidence suggests that patients with depression tend to elicit more positive feelings among clinicians than patients with other severe mental disorders such as borderline personality disorder or schizophrenia. Furthermore, it documents the existence of associations between patients’ severity of depressive symptoms and clinicians’ subjective reactions, although the results regarding which specific countertransference patterns are evoked in relation to the different phases of the treatment are not entirely consistent. Lastly, growing evidence suggests the presence of clinicians’ specific emotional reactions towards patients with suicidal ideation and behavior. Full article

In this paper, the results of the project INSESS-COVID19 are presented, as part of a special call owing to help in the COVID19 crisis in Catalonia. The technological infrastructure and methodology developed in this project allows the quick screening of a territory for a quick a reliable diagnosis in front of an unexpected situation by providing relevant decisional information to support informed decision-making and strategy and policy design. One of the challenges of the project was to extract valuable information from direct participatory processes where specific target profiles of citizens are consulted and to distribute the participation along the whole territory. Having a lot of variables with a moderate number of citizens involved (in this case about 1000) implies the risk of violating statistical secrecy when multivariate relationships are analyzed, thus putting in risk the anonymity of the participants as well as their safety when vulnerable populations are involved, as is the case of INSESS-COVID19. In this paper, the entire data-driven methodology developed in the project is presented and the dealing of the small subgroups of population for statistical secrecy preserving described. The methodology is reusable with any other underlying questionnaire as the data science and reporting parts are totally automatized. Full article