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The course of psychotic disorders typically involves relapses. Early warning signs vary between individuals and are difficult to detect in clinical practice, especially in outpatient settings. Speech provides a quantitative clinical marker for detecting such early warning signs. The EU Horizon project TRUSTING (A TRUSTworthy speech-based AI monitoring system for the prediction of relapse in individuals with schizophrenia) aims to develop and evaluate a speech-based monitoring system for predicting imminent psychotic relapses. The study will examine the potential for prospective relapse prediction, and feasibility and usability of the monitoring system.
In this multicentre observational study, n=240 remitted and at-risk-of-relapse adults with psychotic disorders and a comparison group with n=120 healthy participants (matched by age and sex) will be examined at six sites and in six different languages (German, French, Dutch, English, Czech and Turkish). The follow-up period is 6 months. The TRUSTING smartphone app will be used to collect weekly voice recordings through speech tasks; information on medication adherence, substance use, mood, anxiety and sleep quality; and motor data from a tapping task. Primary endpoints encompass model performance for relapse prediction, user adherence, transcription quality, usability of recordings and overall system usability. The primary analysis of user adherence, transcription quality, usability of recordings and overall system usability will be an unadjusted description of the respective proportions using 95% Wilson confidence intervals. Regarding relapse prediction, the predictive value of the risk estimates for relapse occurrence will be assessed using the area under the receiver operating characteristic curve. Exploratory analysis will be performed on potential speech-based markers associated with relapse risk.
This study has been approved by swissethics (Business Administration System for Ethics Committees number: 2025–01177). Findings from this project will be disseminated through peer-reviewed journal publications and presentations at relevant scientific conferences, as well as at public events related to mental health.
ClinicalTrials.gov ID: NCT07397975.
by Andrea Salas-Ortiz, Marjorie Opuni, José Luis Figueroa, Louis Masankha Banda, Alice Olawo, Spy Munthali, Julius Korir, Barbara Nyambura Thirikwa, Agatha Bula, Navindra Persaud, Sergio A. Bautista-Arredondo
Community-based facilities delivering HIV services to key populations often face significant operational and financial challenges, making the quality of management in these organizations particularly important. A growing body of evidence links management quality to health facility performance. However, research on management in health facilities has focused largely on structural characteristics and formal qualifications, with less attention to the non-cognitive characteristics of managers themselves, particularly personality traits. This is a cross-sectional, quantitative analysis of 45 facilities providing HIV services to key populations in Kenya and Malawi. The analysis includes two stages. We first use k-means cluster analysis on management practice data to identify sub-groups of facilities that share similar management profiles. We then use non-linear logit regression models to predict the probability of facilities belonging to each sub-group as a function of manager personality traits, controlling for manager education, experience, and facility location characteristics. We used the Big Five Inventory to measure personality traits. We found two clusters of facilities with statistically different levels of managerial capacity. The higher-capacity cluster shows stronger financial and people management, more developed performance monitoring and target setting, better operations management, and greater community involvement in financial decisions. In the non-linear logit models, educational achievement was not statistically significant. Manager experience working with HIV key populations and higher scores on the Stability meta-trait were positively and significantly associated with the probability of belonging to the high-managerial capacity cluster. The personality traits of managers, together with their technical and cognitive skills, are relevant to the selection and support of managers in these organizations.Use a socio-technical strategy to identify the use and implications of generative artificial intelligence (GenAI) tools on nursing education and practice.
Descriptive qualitative study.
Online interviews with 32 nursing students, faculty and practitioners between February and April 2024. Data were analysed using the Framework Method.
Theme 1 described participants' use of eight GenAI tools across seven use cases. Theme 2 describes the implications of using GenAI tools on nursing education. The subthemes include (2.1) facing a new pedagogical reality, (2.2) negative sentiments on using GenAI tools in nursing education and (2.3) opportunities to improve nursing education with GenAI tools. Theme 3 describes the implications of using GenAI tools on nursing practice. Subthemes include (3.1) embedding in patient care, (3.2) nursing workflow integration and (3.3) organisational support. Theme 4 describes GenAI capacity-building. Subthemes include (4.1) to develop an AI-ready workforce, (4.2) to promote responsible and ethical use and (4.3) to advance the nursing profession.
Although GenAI tools initially disrupted nursing education, it is only a matter of time before they disrupt nursing practice. Nurses across education and practice settings should be trained in the responsible and ethical use of GenAI tools to mitigate risks and maximise benefits.
GenAI tools will profoundly impact how nurses of today and tomorrow learn and practice the profession. It is crucial for nurses to actively participate in shaping this technology to minimise risks and maximise benefits to the nursing profession and patient care.
This study revealed the socio-technical intricacies of using GenAI tools in nursing education and practice. We also present wicked problems that nurses will face when using GenAI tools.
COREQ.
This study did not include patient or public involvement in its design, conduct or reporting.
FreshRSS 