Abstract. Latent topics and trends in psychological publications were examined to identify hotspots in psychology. Topic modeling was contrasted with a classification-based scientometric approach in order to demonstrate the benefits of the former. Specifically, the psychological publication output in the German-speaking countries containing German- and English-language publications from 1980 to 2016 documented in the PSYNDEX database was analyzed. Topic modeling based on latent Dirichlet allocation (LDA) was applied to a corpus of 314,573 publications. Input for topic modeling was the controlled terms of the publications, that is, a standardized vocabulary of keywords in psychology. Based on these controlled terms, 500 topics were determined and trending topics were identified. Hot topics, indicated by the highest increasing trends in this data, were facets of neuropsychology, online therapy, cross-cultural aspects, traumatization, and visual attention. In conclusion, the findings indicate that topics can reveal more detailed insights into research trends than standardized classifications. Possible applications of this method, limitations, and implications for research synthesis are discussed.

Abstract. For identifying psychological hotspot topics, a mere focus on bibliometric data suffers from a publication delay. To overcome this issue, we introduce Twitter mining of ongoing online communication among scientists for the detection of psychological research topics. Specifically, we collected the entire 69,963 tweets posted between August 2007 and July 2020 from 139 accounts of psychology professors, departments, and research institutes from the German-speaking countries, as well as sections of the German Psychological Society (DGPs). To examine whether Twitter topics are hotspots in terms of indicating future publication trends, 346,361 references in the PSYNDEX database were extracted. For determining the additional value of our approach in contrast to traditional conference analysis, we gathered all available conference programs of the DGPs and its sections since 2010 and compared dates of topic emergence. Results revealed 21 topics addressing societal issues (e.g., COVID-19), methodology (e.g., machine learning), scientific research (e.g., replication crisis), and different areas of psychological research. Ten topics indicated an increasing publication trend, particularly topics related to methodology or scientific transparency. Seven Twitter topics emerged earlier on Twitter than at conferences. A total of four topics could be expected neither by bibliometric forecasting nor conference contents: “methodological issues in meta-analyses”, “playfulness”, “preregistration”, and “mobile brain/body imaging”. Taken together, Twitter mining is a worthwhile endeavor for identifying psychological hotspot topics, especially regarding societal issues, novel research methods, and research transparency in psychology. In order to get the most comprehensive picture of research hotspots, Twitter mining is recommended in addition to bibliometric analyses of publication trends and monitoring of conference topics.

Zusammenfassung. Als erstes dieser Art in Deutschland zielt das Modellprojekt „Qualitätsmonitoring in der ambulanten Psychotherapie” darauf ab, ein modifiziertes Qualitätssicherungs- und Rückmeldeverfahren in der Regelversorgung der gesetzlichen Krankenkassen hinsichtlich seiner Einsetzbarkeit zu untersuchen. Im vorliegenden Beitrag werden die Aspekte der Eingangsdiagnostik, des Rückmeldesystems, der Patientenzufriedenheit, der Therapiedauer und der Evaluation des Projektes durch die Patienten vorgestellt. In der Interventionsgruppe (IG) wurden Checklisten zur Diagnosestellung genutzt und im Gegensatz zur Kontrollgruppe (KG) ebenfalls kontinuierliche Rückmeldungen zum Therapiefortschritt im Verlauf gegeben. Daten zu Therapiebeginn lagen von 1708 Patienten (IG:1031; KG:677) und 245 Therapeuten vor. Die ambulante Psychotherapie stellte sich als effektives Verfahren bei klar psychisch belasteten Personen heraus. In der IG (Einsatz Diagnosechecklisten) wurden im Vergleich zur Kontrollgruppe (KG) insgesamt mehr Diagnosen pro Patient durch die Therapeuten vergeben. Der überwiegende Teil der Therapeuten nutzte die Rückmeldungen für die Therapie und besprach die Ergebnisse mit den Patienten. Konsistent mit der Literatur zu Rückmeldungen in die Psychotherapiepraxis ließen sich frühzeitig im Therapieverlauf durch das Feedbacksystem prädiktive Verlaufsmuster identifizieren. Ferner war die Patientenzufriedenheit in der IG höher (Effektstärke: d = 0.27). Die Behandlungsdauer der IG lag im Rahmen der sich in den unterschiedlichen Kontrollgruppen der Versorgungspraxis ergebenden Behandlungsdauer. Die Zufriedenheit mit dem Modellprojekt ist bei den Patienten am höchsten. Die Patienten erlebten den Einsatz der Rückmeldungen bei vertretbarem Zeitaufwand als wichtig und hilfreich und mit der eigenen Einschätzung übereinstimmend. Die Befunde unterstreichen die Bedeutung von Qualitätssicherungs- und Rückmeldesystemen in der ambulanten Psychotherapie.

Abstract. In our era of accelerated accumulation of knowledge, the manual screening of literature for eligibility is increasingly becoming too labor-intensive for summarizing the current state of knowledge in a timely manner. Recent advances in machine learning and natural language processing promise to reduce the screening workload by automatically detecting unseen references with a high probability of inclusion. As a variety of tools have been developed, the current review provides an overview of their characteristics and performance. A systematic search in various databases yielded 488 eligible reports, revealing 15 tools for screening automation that differed in methodology, features, and accessibility. For the review on the performance of screening tools, 21 studies could be included. In comparison to sampling records randomly, active screening with prioritization approximately halves the screening workload. However, a comparison of tools under equal or at least similar conditions is needed to derive clear recommendations.

Understanding the role of prior knowledge in human learning is essential for predicting, improving, and explaining competence acquisition. However, the size and breadth of this field make it difficult for researchers to glean a comprehensive overview. Hence, we conducted a bibliometric analysis of 13,507 relevant studies published between 1980 and 2021. Abstracts, titles, and metadata were analyzed using text mining and network analysis. The studies investigated 23 topics forming five communities: Education, Learning Environments, Cognitive Processes, Nonacademic Settings, and Language. The investigated knowledge was diverse regarding its types, characteristics, and representations, covering more than 25 academic and non-academic content domains. The most frequently referenced theoretical backgrounds were the 3P Model, Cognitive Load Theory, and Conceptual Change approaches. While our results indicate that prior knowledge is a widely used cross-sectional research topic, there remains a need for more integrative theories of when and how prior knowledge causally affects learning.