Goethe Institute

Abstract Background Making Every Contact Count (MECC) is a person-centred approach to health behaviour change, utilising behavioural science to promote healthy lifestyle choices. Weight-related conversations in mental health settings are particularly important, with users noting gaps in information on medication-related weight gain and support needs. MECC training can address these gaps by improving staff confidence and service delivery, enhancing weight management for individuals with serious mental illness. This study explores staff experiences of weight-related MECC conversations in a mental health setting. Methods This mixed-methods study involved healthcare staff from a National Health Service (NHS) mental health inpatient setting in Northeast England. A quantitative online survey administered pre- MECC training, post-MECC training, and 8-10 weeks later captured staff perspectives. Qualitative interviews were conducted with trained and non-trained staff to explore MECC implementation. Quantitative data were analysed using independent and paired-samples t-tests, while qualitative data underwent thematic analysis, using the COM-B framework that examines Capability, Opportunity, and Motivation as key drivers of behaviour. Results Thirty-six staff completed the pre-training survey, 20 completed the post-training survey, and 25 participated in interviews (15 trained, 10 non-trained staff). Quantitative analysis showed that training improved staff perceptions of confidence and motivation to deliver MECC, though most changes were not statistically significant. Perceptions around opportunity (time, resources, social support) declined at follow-up. Perceived importance and usefulness of MECC also declined over time (p < .05). Qualitative analysis identified barriers and facilitators, mapped to the COM-B model. Trained staff highlighted organisational resources, training structure, and wider determinants of health in supporting MECC delivery, alongside skills in rapport-building. Non-trained staff noted gaps in MECC awareness, recording systems, and training but recognised MECC importance and impact. Both groups identified time constraints, confidence, and the integration of MECC within their professional roles as key factors influencing delivery of weight-related conversations. Conclusion MECC training positively impacted healthcare professionals’ perceptions and confidence in delivering weight-related MECC conversations in mental health settings. While only confidence changes were evident in surveys, qualitative findings showed multifaceted influences on MECC implementation, emphasising the need for sustained support and system-level changes to overcome structural barriers and ensure long-term MECC delivery.

We derive a first-order perturbation formula that links the complex eigenfrequency shift of terahertz resonator-based sensors directly to its complex mode volume V~c. This closed-form expression simultaneously captures the resonance shift, the linewidth change, sensitivity, and figure of merit, and remains valid across a wide range of resonator types by accounting for both the real (confinement) and imaginary (leakage) components of 1/V~c. Full-wave eigenfrequency simulations of a high-Q photonic-crystal cavity and a low-Q split-ring resonator confirm that the model accurately predicts the complex-frequency shift and sensing performance. In contrast, the conventional real-volume approximation ignores linewidth variation and, therefore, fails in low-Q or strongly radiative systems, significantly underestimating sensitivity. The complex-mode-volume framework, thus, offers a unified and quantitative basis for designing and optimizing open terahertz sensors, revealing the trade-offs between field confinement and radiative loss that ultimately limit detection performance.

Intramolecular closure is an effective method to derive polymer rings in which the ends of the parent polymer chains are strongly connected. It is important to develop simple and sensitive methods to discriminate between the intramolecular ring closure and intermolecular cross-linking. Herein, we propose the aggregation-induced emission (AIE) based method to monitor the intramolecular closure of polymer chains. The example polymer of poly(pentafluorophenyl methacrylate)-block-poly(2-(dimethylamino) ethyl methacrylate)-block-poly(pentafluorophenyl methacrylate) (PPFPMA-b-PDMAEMA-b-PPFPMA) is synthesized by RAFT polymerization, and the AIEgens and reactive styryl groups are grafted at the end blocks by reactions with 1-(4-aminophenyl)-1,2,2-triphenylethene and 4-vinylaniline. Along with the electrostatics-mediated intramolecular closure at the dynamic single-chain nanoparticle (SCNP) via ultraviolet (UV)-irradiated radical reaction toward the polymer rings, the AIEgens become highly restricted at the individual cross-linked cluster, giving a rather bright, spectrally narrow emission at 417 nm. In contrast, multiclusters are present at the intermolecularly cross-linked aggregate which are strongly coupled giving an additional red-shifted emission at 480 nm. The resulting AIEgen-functionalized polymer rings therefore combine sharply defined fluorescence signatures with enhanced stability in poor solvents, offering promise as functional optical materials and potential diagnostic probes.

According to the noisy channel framework of sentence processing, communication can succeed even when the input is corrupted because comprehenders rationally infer the speaker's intended meaning based on the prior probability of the literal interpretation and the probability that the input has been corrupted by noise. To test whether and under what conditions comprehenders consider word exchanges as a possible source of corruption, we ran five experiments on processing three types of simple German sentences: subject‐before‐object sentences (SO), object‐before‐subject sentences (OS), and passive sentences. Critical sentences had implausible meanings, but could be “repaired” by exchanging function words or by exchanging nouns. Experiments 1 through 4 presented sentences along with yes‐no questions to probe interpretation. Implausible SO and passive sentences consistently elicited few nonliteral interpretations, whereas many nonliteral interpretations were given to implausible OS sentences. This was true regardless of whether word exchanges had to cross a main verb or an auxiliary, and it was more pronounced if the overall proportion of implausible sentences was low. We conclude that when answering yes‐no questions, word exchanges are considered with function words of the same syntactic category, but not with nouns, and only when they result in a more likely syntactic structure. Experiment 5 showed that when explicitly asked to correct implausible sentences, comprehenders use noun exchanges frequently. We propose that the results for both yes‐no questions and explicit corrections follow if the prior probability assigned to implausible sentences differs between tasks.

The phase diagram of QCD probably exhibits a moat regime over a large region of temperature T and chemical potential μ≠0. A moat regime is characterized by quasiparticle moatons (pions) whose energy is minimal at nonzero spatial momentum. At μ≠0, higher mass dimension operators play a critical role in a moat regime. At dimension six, there are nine possible gauge-invariant couplings between scalars and photons. For back-to-back dilepton production, only one operator contributes, which significantly enhances production near a moat threshold. This enhancement is an experimental signature of moatons.

How to identify suspected infection for sepsis surveillance purposes remains a well-recognised challenge. This study aimed to operationalise suspected infection for sepsis surveillance by developing an interpretable machine learning (ML) model for retrospective identification of patients with sepsis. This multicentre cohort and machine learning study was conducted in two Dutch tertiary care hospitals. Adult patients with a quick Sequential Organ Failure assessment (qSOFA) ≥2 were included. Exclusion criteria included admission to the intensive care unit, transfer to or from another hospital, or patient refusal to reuse data. Cohort one consisted of patients admitted to the Emergency Department (ED) of hospital A between 01/01/2019 and 12/31/2019, to investigate community-onset sepsis. An external validation cohort of ED patients was obtained from hospital B between 01/01/2021 and 06/03/2022. Cohort two included hospitalised patients from hospital A between 01/01/2021 and 06/01/2022, to investigate hospital-onset sepsis. Objective data were extracted from electronic health records. Seven ML methods, including gradient boosting, random forest, logistic regression, decision trees, support vector machines, K nearest neighbours and stochastic gradient descent, were trained to identify sepsis with manual chart review as reference standard. The F1 score (harmonic mean of precision and recall), sensitivity and specificity were used as evaluation metrics. The best performing ML method was compared with other commonly used suspected infection proxies, including the Sepsis-3 definition, an adapted Adult Sepsis Event (ASE) definition and International Classification of Diseases (ICD) codes. In the ED cohort, 655 patients were included (male: 355 (54.2%), female: 300 (45.8%)) and 240 (36.6%) had sepsis. For community-onset sepsis, gradient boosting performed best with an F1 score of 85.9%, a sensitivity of 91.1% (95%-CI 83.4-95.4%) and a specificity of 89.0% (95%-CI 83.4-92.8%). Most model features reflected either the inflammatory response (CRP, body temperature) or actions taken when an infection is suspected (antibiotic administration, microbial culture). In the external validation cohort, 185 patients were included (male: 94 (50.8%), female: 91 (49.2%)) and 54 (29.2%) had sepsis. External validation yielded an F1 score of 85.7%, a sensitivity of 87.5% (95%-CI 75.3-94.1%) and a specificity of 92.5% (95%-CI 85.9-96.2%). The gradient boosting model outperformed other commonly used proxies for suspected infection in terms of sensitivity, achieving 91.1% (95% CI: 83.4-95.4%), compared to Sepsis-3 with 78.9% (95% CI: 69.4-86.0%), the adapted ASE with 85.6% (95% CI: 76.8-91.4%), and ICD codes with 33.3% (95% CI: 24.5-43.6%). In the hospitalised cohort, 493 patients were included (male: 265 (53.8%), female: 228 (46.2%)) and 129 (26.2%) had sepsis. For hospital-onset sepsis, logistic regression had the highest F1 score (52.2%). Sensitivity was 58.1% (95%-CI 40.6-75.5%) and specificity was 82.9% (95%-CI 76.0-89.8%). ED patients meeting ≥2 qSOFA criteria can be accurately classified as having suspected infection or not by a gradient boosting algorithm, outperforming common suspected infection definitions for sepsis surveillance. Including the inflammatory response in the suspected infection surveillance definition may enhance the accuracy and objectivity of sepsis surveillance. Future research is needed to validate the algorithm using other organ dysfunction criteria and in international settings. None.

This paper explores the evolving role of grammar instruction in English Language Teaching (ELT), examining contemporary trends and methodologies that influence its integration into language classrooms. Focusing on student preferences, the study investigates the effectiveness of both traditional grammar teaching methods and the incorporation of modern, technology-enhanced approaches. Through an analysis of survey data from students across various ELT contexts, the research highlights the continued importance of explicit grammar instruction, while also recognizing the growing appeal of hybrid approaches that combine explicit and implicit learning. The findings emphasize the increasing use of digital tools for grammar practice, showing how technology can support autonomous learning and provide personalized feedback. The paper argues for a balanced approach to grammar teaching, combining structured, rule-based instruction with communicative tasks and technological resources. It concludes with recommendations for future research and teaching practices, emphasizing the need for adaptable, student-centered methodologies in grammar instruction to address the diverse needs of modern learners.

We consider a problem borrowed from heavy-ion collisions, namely, the formation of bound states, as for example the deuteron, in the nonrelativistic regime by using the techniques of Lindblad dynamics considering an open quantum system. We show that the position-space Lindblad equation can be reformulated in terms of a diffusion-advection equation with sources and therefore provides a hydrodynamical formulation of a dissipative quantum master equation. Making use of this advanced machinery and these insights, we describe the possible formation of a bound state, which is realized by a P\"oschl-Teller-like potential, of a particle in interaction with a heat bath in a one-dimensional setting and discuss general aspects of open quantum systems, such as decoherence, entropy production, purity, thermalization time, and thermalization. Finally, we also show an example of a much deeper potential, where we allow for three bound states, just in the spirit of quarkonia.