Ketamine-assisted psychotherapy (KAP) is considered a promising treatment strategy in psychiatric disorders, combining psychotherapeutic interventions with the rapid-acting pharmacological effects of ketamine towards enhancing outcomes. Although KAP has been practiced for many years across different diagnoses, evidence regarding optimal treatment methods and working mechanisms remains limited and has only partly been synthesized. This systematic review provides a historical overview of studies published up to 2025, that combined ketamine and psychotherapy for the treatment of mental disorders. Psychotherapeutic approaches were categorized into their overarching psychological paradigms: psychoanalytic and psychodynamic, behavioral, humanistic, cognitive-behavioral, integrative/holistic, and other therapeutic frameworks. 64 articles, reporting on 72 studies, were included in this review, of which only 11 were randomized controlled trials (RCTs), highlighting the limited representation of RCTs in the overall body of evidence. Diverse therapeutic approaches were found, most were classified into the 'other' category (n=24). Results suggest transdiagnostic effectiveness for indications including major depressive disorder, posttraumatic stress disorder, obsessive-compulsive disorder, substance use disorders and eating disorders. KAP, compared to only psychotherapy, was observed to enhance treatment engagement, symptom reduction, and duration of treatment response. Only two studies randomized psychotherapy in the context of ketamine treatment, neither of which reported added effects for psychotherapy. One of those studies also randomized ketamine and did not find a significant interaction effect. This limited evidence does not support added benefit from psychotherapy nor evidence of synergy. Methodological heterogeneity and the limited number of trials that used a psychotherapy control group, restrict definitive conclusions. Clinical effects are generally reported to be quite positive across psychiatric indications, however, there is a clear need for well-controlled trials to test for potential additive or synergistic effects of the combined use of ketamine and psychotherapy and to identify optimal therapeutic protocols.

This three-level meta-analysis examined the effects of post-detention aftercare programs for adolescent and young adult offenders on several types of criminal recidivism (primary outcomes) and dynamic risk and protective factors for criminal behavior (secondary outcomes). A systematic search was conducted in the databases APA PsycInfo, Medline, ERIC, Web of Science Core Collection, SocINDEX, and Google Scholar, resulting in 26 relevant studies that produced 259 effect sizes. The results revealed a small overall effect (g=0.26). Aftercare was effective for both primary (g=0.23) and secondary (g=0.35) outcomes. Smaller effects were found for general offenses (g=0.23) and violent offenses (g=0.26), a somewhat larger effect for minor offenses (g=0.39), and no effect for substance abuse offenses (g=-0.04). Larger effects were found for arrests (g=0.25) compared to convicted offenses (g=0.12). Moderator analyses showed that mentoring and multi-modal interventions were effective, whereas family interventions as well as service and surveillance interventions were not effective. Positive changes in skills, substance abuse, family functioning, externalizing problems, internalizing problems, and general health were associated with larger intervention effects on recidivism, and thus may be considered important levers of change in post-detention aftercare programs. It was concluded that aftercare programs can fulfill a crucial role in deterring youth from re-offending. Future research should focus on refining aftercare programs and identifying program components that contribute to the success of aftercare for young offenders.

"Core threats" are the ultimate feared consequences motivating avoidance behaviors and underpinning surface-level fears. For example, the surface fear of contamination may stem from core threats such as death, suffering, or harming loved ones. Whereas traditional models of anxiety recognize the role of motivation, they have not emphasized how global motivations shape threat appraisal and avoidance. We distinguish core threats from core beliefs: while core beliefs describe how individuals view themselves, others, and the world (which typically influence the likelihood of core threats), core threats specify why outcomes matter emotionally-what makes them motivating (i.e., why are they individually costly). Core threats provide the motivational structure that organizes diverse fears across anxiety disorders and beyond. They help explain generalization, persistence, and relapse. Phenomenologically, core threats are highly individualized, reflecting personal values over diagnostic categories. Contemporary models of treatment of anxiety often emphasize metacognitive regulation processes while treating fear content as interchangeable or secondary. We propose a complementary perspective: motivationally guided appraisals determine what becomes threatening and why. We formalize this perspective in a computational framework that distinguishes motivational drivers of avoidance, including core threats, distress intolerance, and exaggerated duration. Just as categories are organized around central exemplars, fear structures are organized around motivationally central threats. Conditioning research mirrors this principle: fear generalizes more strongly from central exemplars and remains sensitive to the value of the underlying outcome. Targeting core threats-the motivational focus of fear-offers a pathway to engage broader fear networks, improve generalization, reduce relapse, and guide personalized interventions.

Emotional disorders such as depression and anxiety affect millions globally and pose a significant burden on public health. Personalized treatment approaches using machine learning (ML) to predict treatment response could revolutionize treatment strategies. However, there is limited evidence as to whether ML is successful in predicting treatment outcomes. This meta-analysis aims to evaluate the accuracy of ML algorithms in predicting binary treatment response (responder vs. non-responder) to evidence-based psychotherapies, pharmacotherapies, and other treatments for emotional disorders, and to examine moderators of prediction accuracy. Following PRISMA guidelines, a comprehensive literature search was conducted across PubMed and PsycINFO from January 1st, 2010 to March 27th, 2025. Studies were included if they used ML methods to predict treatment response in patients with emotional disorders. Data were extracted on sample size, type of treatment, predictors used, ML methods, and prediction accuracy. Meta-analytic techniques were used to synthesize findings and identify moderators of prediction accuracy. Out of 3816 non-duplicate records, 155 studies met inclusion criteria. The overall mean prediction accuracy was 0.76 (95% CI: 0.74-0.78), and the mean area under the curve was 0.80 indicating good discrimination. The average sensitivity and specificity were 0.73 and 0.75, respectively. Moderator analyses indicated that studies using more robust cross-validation procedures exhibited higher prediction accuracy. Neuroimaging data as predictors were associated with higher accuracy compared to clinical and demographic data. Moreover, results indicated that studies with larger responder rates, as well as those that did not correct for imbalances in outcome rates, were associated with higher prediction accuracy. ML methods show promise in predicting treatment response for emotional disorders, with varying degrees of accuracy depending on the type of predictors used and the rigor of methodological procedures implemented. Future research should focus on improving methodological integrity and exploring the integration of multimodal data to enhance prediction accuracy.

We applied a two-stage Bayesian analysis to examine the relationship between the jumping to conclusions (JTC) bias (measured on the beads, box, fishing, and/or survey tasks) and delusional ideation (measured on the Peters et al. Delusions Inventory or the Community Assessment of Psychic Experiences). MEDLINE, PsycINFO, Scopus, Web of Science, and five previous JTC reviews were searched for eligible studies published between 1988 and December 2024. Risk of bias was assessed using an adapted version of the Agency for Healthcare Research and Quality assessment tool. Forty-two studies (n=7604) were identified as meeting inclusion criteria, with four subgroups: general population (n=7538), psychosis with current delusions (n=449), psychosis without current delusions (n=29), and clinical control (n=77). Our first analysis presented a meta-analysis of correlations between delusional ideation and JTC. Our second analysis presented a zero-inflated Poisson regression, assessing change in JTC across variable delusional ideation scores. Impact of data quality was assessed in both analyses. No meaningful relationships between JTC and delusional ideation were found in the full dataset or any subgroup, but the subgroup with current delusions showed a relatively greater JTC bias than the general population subgroup. Data-quality procedures did not impact results. Overall, results suggest that the JTC bias is not related to delusional ideation, or indexes of delusional ideation and/or JTC used in this meta-analysis are poor measures of these constructs. Our results did not assess delusional severity, thus interpretations in clinical populations are limited. PROSPERO https://www.crd.york.ac.uk/PROSPERO/view/CRD42021266402.