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Bibliotherapy, a therapeutic approach that uses books and reading materials to promote psychological well-being and personal growth, has become more prevalent in recent years. This scientometric study aims to provide a comprehensive view of the bibliotherapy research landscape by highlighting its evolution, trends, and noteworthy contributions using Biblioshiny and VOSviewer.

The academic literature on bibliotherapy is evaluated in-depth in this study utilizing scientometric techniques, including citation and co-citation analysis. A thorough search of the Scopus database revealed 1,703 papers between 1942 and 2023 that dealt with bibliotherapy. For data analysis, the renowned applications Biblioshiny and VOSViewer are employed.

The study reveals that the output of publications has fluctuated, reflecting scholarly interest in this discipline. The distribution of research across various countries, organizations and academic subjects is investigated further to highlight the diverse and global extent of bibliotherapy research. By analyzing co-citation networks and locating pertinent publications and authors, this scientometric method analyzes the intellectual structure of bibliotherapy research.

Bibliometric analysis enriches the theoretical understanding of bibliotherapy by unveiling the networks, influential works and existing gaps in the literature, thus guiding a more informed and collaborative approach to future research and practice in the domain.

Employing bibliometric analysis in bibliotherapy can refine practices and training programs, ensuring they are evidence-based and practical, enhancing the quality of therapeutic services provided to individuals.

It is a valuable resource for academics, practitioners and policymakers interested in the field since it offers a thorough and current assessment of the bibliotherapy research landscape. The findings of this study have the potential to steer future research, guide the development of bibliotherapeutic interventions supported by evidence and enhance the use of bibliotherapy as a therapeutic modality.

The future increase of chronic diseases in the world requires new challenges in the health domain to improve patients' care from the point of view of the organizational processes, clinical pathways and technological solutions of digital health. For this reason, the present paper aims to focus on the study and application of well-known clinical practices and efficient organizational approaches through an innovative model (TALIsMAn) to support new care process redesign and digitalization for chronic patients.

In addition to specific clinical models employed to manage chronic conditions such as the Population Health Management and Chronic Care Model, we introduce a Business Process Management methodology implementation supported by a set of e-health technologies, in order to manage Care Pathways (CPs) digitalization and procedures improvement.

This study shows that telemedicine services with advanced devices and technologies are not enough to provide significant changes in the healthcare sector if other key aspects such as health processes, organizational systems, interactions between actors and responsibilities are not considered and improved. Therefore, new clinical models and organizational approaches are necessary together with a deep technological change, otherwise, theoretical benefits given by telemedicine services, which often employ advanced Information and Communication Technology (ICT) systems and devices, may not be translated into effective enhancements. They are obtained not only through the implementation of single telemedicine services, but integrating them in a wider digital ecosystem, where clinicians are supported in different clinical steps they have to perform.

The present work defines a novel methodological framework based on organizational, clinical and technological innovation, in order to redesign the territorial care for people with chronic diseases. This innovative ecosystem applied in the Italian research project TALIsMAn is based on the concept of a continuum of care and digitalization of CPs supported by Business Process Management System and telemedicine services. The main goal is to organize the different socio-medical activities in a unique and integrated IT system that should be sustainable, scalable and replicable.

This study aims to investigate the prevalence and characteristics of dysphagia among adults with intellectual disabilities (ID) referred to speech and language therapy for swallow assessment, providing information on the demographic characteristics, referral trends, co-occurring health conditions and reasons for referrals highlighting the complex health-care needs of this population.

This study used a standardised patient data extraction method over a six-month period involving 74 adults with ID referred to speech and language therapy for swallow assessment.

This study revealed a high prevalence of dysphagia among adults with ID referred to speech and language therapy for swallow assessment. Increasing age and severity of ID were associated with an increased likelihood of swallowing difficulties. Co-occurring health conditions such as mobility difficulties, epilepsy and gastrointestinal conditions were prevalent, suggesting that adults with ID and swallowing difficulties are often living with complex health conditions. Choking incidents and hospital admissions were primary reasons for referral.

This study stresses the pressing need for strategies to mitigate risks associated with choking incidents and hospital admission among this vulnerable population. Possible limitations include a reliance on referral and the focus being on a single service over a short period which may limit generalisation to the wider ID population.

This study emphasises the need to understand each person’s unique profile of health needs and the value of a specialised speech and language therapy service.

The importance of increasing awareness among caregivers and medical experts is highlighted.

The findings underscore the importance of tailored assessment, caregiver involvement and heightened interdisciplinary awareness to effectively manage dysphagia in individuals with ID.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.