The environment in healthcare facilities can influence health and recovery of service users and furthermore contribute to healthy workplaces for staff. The concept of therapeutic landscapes seems to be a promising approach in this context. The aim of this qualitative meta-analysis is to review the effects of therapeutic landscapes for different stakeholders in psychiatric care facilities. A systematic literature search was conducted in the four data bases PubMed, PsycInfo, CINAHL, and Web of Science. Thirteen predominately qualitative studies were included in this qualitative meta-analysis. The methodological quality of these qualitative studies was assessed, using an adapted version of the Journal Article Reporting Standards for Qualitative Research, and a thematic analysis was conducted. The results were categorised into the three main themes of the physical (built and natural), social, and symbolic dimensions of the therapeutic landscape. Given the heterogeneity of the summarised data and an overall methodological quality of the included studies that can be rated as medium, the results should be interpreted with caution. Current findings are based almost exclusively on qualitative studies. Therefore, there is a need for quantitative study designs that investigate the relationship between specific environmental elements and mental health outcomes for different stakeholders in psychiatric facilities.

Artificial intelligence (AI) techniques such as natural language processing can be used to construct personality scales. The present study compares the psychometric properties of scales in the domains of Emotional Stability and Conscientiousness developed by experts with those of scales in these domains that were generated by the AI chatbot ChatGPT. We conducted two online studies ( N = 155 and N = 459) in which respondents completed inter alia the expert-developed and the AI-generated scales. Results show that all four scales demonstrated similar reliability and similar content, construct, and criterion validity, but differences regarding fit with a latent measurement model and measurement invariance across genders. We discuss the implications of these findings for the integration of ChatGPT into the process of scale construction and give general recommendations for prompting ChatGPT for automatic item generation.

Large language models (LLMs) have gained increased attention as tools for automatic item and scale generation in psychological assessment. Yet, it is not known how prompt instructions can affect the psychometric quality of LLM-generated items and scales. To test whether different prompt instructions influence the quality of generated items and scales, a general-purpose LLM (GPT4o) was prompted with seven different prompt instructions (one adhering to prompt engineering principles solely and six combining these principles with five different psychometric characteristics). Items were generated for two constructs, one established (emotional stability) and one rather novel (climate change anxiety). The LLM-generated items and scales were evaluated in terms of content validity, item difficulty, model fit, factor structure, reliability, and validity. Overall, there were no systematic differences regarding content validity, item difficulty, model fit, reliability and convergent validity, but unsystematic differences in terms of validity.

This experimental study examined whether faking affected a construct‐focused situational judgment test (SJT) measuring self‐organization. Using a within‐subjects design, the SJT and a single‐statement self‐report were administered to employees of a federal agency who were students at a cooperative university affiliated with that agency ( N = 256), under two conditions: honest responding and faking good. The SJT used knowledge instructions (“How should one behave?”) and an expert‐based scoring method whereby participants' responses were compared with those of subject‐matter experts. Construct validity was assessed using correlations with Big Five personality traits; criterion validity was evaluated based on correlations with participants' grade point averages (GPAs). Faking changed scores substantially on the single‐statement self‐report ( d = 1.04) but only modestly on the SJT ( d = 0.25). Structural equation modeling revealed that the construct validity of the single‐statement self‐report was reduced under faking conditions, whereas that of the SJT remained stable, showing consistent associations with Conscientiousness and Agreeableness. The criterion validity of the SJT was unaffected or even slightly enhanced under faking conditions. Supplementary analyses using binary scoring replicated prior findings of stronger effects of faking on single‐statement self‐report than on SJT scale scores but showed no detrimental impact on validity. These results suggest that construct‐focused SJTs are less vulnerable to faking than are single‐statement self‐reports, thus supporting their application in high‐stakes selection contexts.

Zusammenfassung: Soft Skills können als berufsbezogene Ressourcen beschrieben werden, die im Zusammenhang mit Erleben, Denken und Verhalten stehen. Das Modell der Big Five Soft Skills beschreibt die fünf Domänen Extraversion (im Beruf), Soziale Kompetenz (Verträglichkeit im Beruf), Disziplin (Gewissenhaftigkeit im Beruf), Belastbarkeit (Emotionale Stabilität im Beruf) und Geistige Flexibilität (Offenheit im Beruf). Anhand von zwei bevölkerungsheterogenen Stichproben ( N = 264, N = 291) zeigen wir, dass selbstberichtete Soft Skills mit Erfolgskriterien wie Bildungsabschluss, Einkommen, Gesundheit, Arbeitszufriedenheit, Kündigungsabsicht und Burnoutrisiko zusammenhängen, teilweise über kognitive Fähigkeiten und Persönlichkeitseigenschaften hinaus. Anhand einer studentischen Stichprobe ( N = 262) zeigen wir, dass selbstberichtete Soft Skills Erfolg im Studium vorhersagen können, selbst wenn die Studierenden instruiert werden, wie in einer Bewerbungssituation zu antworten. Die Ergebnisse zeigen außerdem, dass die Beschreibung von spezifischen Facetten vs. globalen Domänen (bspw. Kontaktfähigkeit, Durchsetzungsfähigkeit, Begeisterungsfähigkeit vs. globale Extraversion) eine bessere Vorhersage von Außenkriterien und ein tieferes Verständnis von Zusammenhängen ermöglicht.