bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perspectives on intersectoral collaboration and citizen participation in community-based prevention
Authors: Sonneveld J M , Bruijning J E , Jerković-Ćosić K , de Wit N J 
Published: October 2024

Abstract

Background
Challenges in healthcare, such as rising healthcare costs, shortages of healthcare staff, and health disparities, have made many countries focus on intersectoral collaboration (IC) and citizen participation (CP) in community-based prevention (CBP) and health promotion (HP). Previous research on IC and CP has addressed the importance of exchanging perspectives to arrive at a shared vision and ambition. Our study is part of a long-term action research project conducted in two neighborhoods in the Netherlands. It aims to provide insight into stakeholders’ perspectives on IC and CP in CBP and HP to facilitate building partnerships.

Methods
Data collection takes place from March to June 2024. As part of a Q-study, we perform post-sort interviews. The Q-methodology is based on sorting statements concerning various aspects of IC and CP in CBP and HP (e.g., roles and tasks, involvement of citizens, communication, perceived barriers, etc.). We aim to include 40-50 participants from the aforementioned neighborhoods with diverse backgrounds. Participants sort 40 statements (Q-set) into a fixed-scaled framework ranging from -4 (disagree) to + 4 (agree). The interview is about individual sorting and perspectives on their own role within the collaboration.

Results
So far, the results of 10 participants with backgrounds in health care, social work, sports, local authority, volunteer work, and public health have been analyzed.Preliminary data synthesis shows six themes concerning shared purpose, collaboration partners, social collaboration competencies, personal motivation, conditions, and collaboration organization.

Conclusions
The preliminary themes overlap with findings from previous research. We will adapt existing models to reflect on our findings and enrich the literature with new themes or insights in the specific context. The interviews will help interpret the Q-sorts and develop follow-up actions in the action research projects in the participating neighborhoods.

Key messages
• Insights in stakeholders’ perspectives are valuable for building local collaborative partnerships.
• Shared purpose, collaboration partners, social collaboration competencies, personal motivation, conditions, and collaboration organization are themes of interest to intersectoral collaboration.
A new digital model for Italian Integrated Home Care: strengths, barriers, and future implications
Authors: Melnyk A , Gentili A , Beccia F , Causio F A , Battilomo S , Paone S , Borghini A , Bartolo M , Chiarolla E , Cascini F 
Published: October 2024

Abstract

Issue/Problem
After the COVID-19 pandemic, Italy’s National Health Service (NHS) is transforming by adopting telemedicine through its National Recovery and Resilience Plan, aiming to modernize healthcare delivery and ensure equity. In this plan Territorial Coordination Centers (COT) were designed to improve patient access and continuity of care. However, regulatory challenges hinder the widespread adoption of telemedicine. Our work describes the technical, structural, and operational aspects of the new Italian Integrated Home Care (IHC) digital model, addressing strengths, barriers, and future implications.

Description of the problem
The implementation of the IHC digital model faces multiple challenges. Italy’s poor technical infrastructure, especially in rural areas, hampers access to critical services. Resistance to change among rural systems and the elderly, coupled with low digital literacy, inhibits telemedicine adoption. Fragmentation in the NHS leads to resource disparities, particularly disadvantaging southern regions. Italy also grapples with a severe shortage of healthcare professionals, posing a threat to the model’s sustainability.

Results
The National Digital Health Agency enacts the IHC model by overseeing telemedicine services through a cloud-based protocol. Each Region provides “Basic Telemedicine Services”: healthcare professionals’ and patient teleconsultation, telemonitoring, teleassistance. Communication between services, infrastructures, and local health authorities is facilitated by a modular interoperability layer, ensuring data standards and quality. A national telemedicine dissemination portal will contain data on all telemedicine solutions provided.

Lessons
Clear communication is essential for telemedicine acceptance. COTs enhance coordination and management. Bridging the technology gap for disadvantaged groups, supporting frontline employees and addressing adoption factors is vital to overcome the challenges faced.

Key messages
• The National Digital Health Agency drives telemedicine services under the IHC model. Each region offers Basic Telemedicine Services, ensuring data standards, quality and interoperability of data.
• Italy’s telemedicine adoption faces poor infrastructure, resistance and workforce shortages. Focusing on equity, healthcare workers support and bridging the technology gap will be key.
Exploring Empathy Dynamics in Virtual Patient Training
Authors: Petrova G , Kostadinov K , Lalova V , Ivanova S 
Published: October 2024

Abstract

Background
This study investigates the manifestation of empathy among nursing students and teachers during virtual patient training with VR glasses at the Medical Simulation Training Centre of Medical University - Plovdiv. The pilot training, conducted from March 22 to April 12, 2024, was part of the Erasmus+ project ‘Caring violent child safely in child psychiatric and residential units (Safe4Child) 2021-1-FI01-KA220-HED-000032106.’ Participants were divided into two focus groups: third-year nursing students and lecturers teaching specialized academic disciplines in nursing.

Results
A total of 34 participants partook in the study, comprising 16 instructors with basic nursing education and 16 third-year nursing students. Students exhibited considerable empathy towards the virtual child patient, expressing an inclination to provide assistance, acknowledging the necessity for personalized care, and displaying a curiosity about the patient’s experiences. They conveyed sentiments such as ‘I felt the need to help the child/patient’, ‘the child needs individual work with a nurse’ and ‘I want to understand the child’s experiences.’ In contrast, instructors demonstrated more reserved emotional responses, characterized by professionalism and a focus on offering comfort. They expressed, ‘Normal feelings...but you want to hug the child and comfort him.’

Conclusions
The study unveiled a marked difference in empathy expression between students and instructors, with students displaying stronger emotional reactions. These findings underscore the need to recognize and address individual variances in empathy expression within nursing education. Tailored strategies should be devised to bolster empathy development in both students and instructors during virtual patient training. Further investigation is warranted to explore effective methodologies for optimizing empathy development and fostering compassionate patient care in nursing education.

Key messages
• A Comparative Analysis of Nursing Students and Instructors Using VR Glasses.
• Erasmus+ project (Safe4Child) 2021-1-FI01-KA220-HED-000032106.
Labour migration and mental health of left-behind children in Georgia: a mixed methods study
Authors: Antia K , Račaitė J , Šurkienė G , Winkler V 
Published: October 2024

Abstract

Background
Global migration is on the rise, and as a result, millions of children are left in their home countries while their parents migrate abroad. Little is known about the mental health of left-behind children (LBC) in Eastern Europe. The study addresses this research gap in Georgia, a leading migrant-sending country in the region.

Methods
Applying convergent parallel design, this mixed methods research integrates a qualitative study with schoolteachers from six public schools in Samegrelo and Guria regions (6 focus group discussions and 6 in-depth interviews) (i); a school survey with 933 adolescents (ii) from 18 public schools, (12-18 age group, internalizing and externalizing problems assessed with Aschenbach’s “Youth Self-Report” questionnaire), and a qualitative study using life-narrative interviews with 39 adolescents (iii). We analysed qualitative data using reflexive thematic analyses and applied multiple linear regression to analyse quantitative data.

Results
The Quantitative and qualitative findings converge in some respects while diverging in others. Schoolteachers perceive migratory separation as a traumatizing life event, making children vulnerable to mental health issues. Similarly, schoolchildren perceive separation from parents as a stressful life event; however, they also acknowledge positive aspects of migrant labour, such as material benefits. From the perception of LBC, close family ties, friendships, school support, and optimism help with coping. This study also revealed a gender disparity, with boys exhibiting more rule-breaking behaviour and girls performing worse on most Youth Self- Reported syndrome scales. Regression analysis showed associations between adolescents’ problem behaviours and relationships with family members, friendships, and school environment.

Conclusions
This research found that parental migration effects on children are complex and depend on individual and transnational family characteristics.

Key messages
• Left-behind children may need emotional and academic support.
• Strengthening family ties, supportive school environment and friendships contribute to adolescents’ coping with parental migration.
Population Health Management effectiveness in the primary healthcare setting: a systematic review
Authors: McNamara R , Nour N , Mannix M , Browne J , Donohue S 
Published: October 2024

Abstract

Background
Population Health Management (PHM) has shown promise as a tool in the improvement of health at scale. It works by; harnessing health-related datasets, allowing for population segmentation and risk stratification, and targeting interventions to yield positive health impact. Prior reviews on PHM, have sought to define PHM and assess feasibility. To date, there have been no systematic reviews that assess the effectiveness of PHM in achieving some or all of the quintuple aims of PHM (WHO 2023). The aim of this study is to determine the value and impact of a PHM approach in managing ambulatory care sensitive conditions (ACSCs) in the primary healthcare setting.

Methods
This systematic review is underpinned by a Population-Intervention-Comparator-Outcome (PICO) framework. The PROSPERO-registered study commenced in February 2024 for papers published since January 2000. Pubmed, Cinahl and Web of Science databases were used. Titles and abstracts of studies were reviewed using agreed eligibility criteria to select studies for inclusion and risk of bias assessment. Data abstraction and synthesis determined the effectiveness of PHM against agreed outcome measures.

Results
The search yielded 427 articles of which 19 were included. Studies addressed diabetes, hypertension, atrial fibrillation, asthma and comorbid conditions. Nearly all studies were based in the U.S.A. Almost 50% of studies showed a significant improvement in health outcomes following PHM. Study features that corresponded with positive health outcomes included; the use of population management tools, the employment of disease/population health managers and the mobilisation of medication reviews/education.

Conclusions
The challenges facing communities and health services require an alternative innovative approach to health such as a PHM approach. The results of this study will inform a larger body of work which seeks to establish a PHM approach to the delivery of services in the Mid-West region of Ireland.

Key messages
• Population health management (PHM) could be a viable mechanism for health services to serve increasing need and multi-morbid populations.
• This is the first systematic review of PHM against the quintuple aims of PHM, specifically in the management of ambulatory care sensitive conditions in primary healthcare.
Effects of a multidisciplinary program with physical exercise in pediatric obesity
Authors: Meneses C , Lopes A , Fonseca F , Eusébio A , Peixoto R , Vilaça-Alves J , Santos C , Moreira T 
Published: October 2024

Abstract

Background
Childhood obesity is a growing public health concern worldwide, with prevalence rates increasing in recent decades. This epidemic not only predisposes children to immediate health risks such as cardiovascular disease and musculoskeletal problems but also significantly increases their risk of obesity-related comorbidities in adulthood. Recognizing the multifactorial nature of pediatric obesity, interventions targeting lifestyle modifications are crucial to its control. Among these interventions, physical exercise emerges as a cornerstone in combating pediatric obesity.

Purpose
Our study examines the effectiveness of a twelve-week online physical exercise program on body composition and functional capacity in children and adolescents with obesity.

Methods
The sample consists of 30 participants (9 girls and 21 boys, aged 11-17 y) from the Pediatrics Department of Senhora da Oliveira Hospital in Guimarães (HSOG). Using standardized protocols, anthropometric measures (body weight and waist circumference) were measured. Functional capacity was assessed through squats, push-ups, and elbow plank tests (maximum repetitions in 60 seconds). All analyses were done using SPSS software, and the alpha level was 5%.

Results
Significant impacts of the exercise program were observed in body weight and waist circumference variables. Multivariate tests indicated a statistically significant interaction between the Moment*Groups factors (weight p = 0.040; waist circumference p = 0.007). Moreover, functional capacity was also influenced by the exercise program (squat p = 0.034; push-ups p = 0.013; and elbow plank p = 0.025).

Conclusions
Our results underscore the significant public health impact of integrating physical exercise into a multidisciplinary program for pediatric obesity. Innovative use of online interventions facilitated broader reach and accessibility, resulting in measurable improvements in body weight, waist circumference, and functional capacity among participants.

Key messages
• Integrating exercise into multidisciplinary programs improves pediatric obesity outcomes.
• Online interventions broaden accessibility and effectiveness in preventing childhood obesity.
Predicting diabetes prognosis using machine learning techniques in a Hungarian clinical database
Authors: Nagy A , Bata R , Ghanem A , Móré M 
Published: October 2024

Abstract

Background
Chronic conditions such as type 2 diabetes mellitus have great impact on patients’ quality of life. Although clinical databases provide a perfect base for research aimed at improving diabetes care, analyzing such databases requires extensive pre-processing, mainly due to the large amount of unstructured data. Our study aimed to present the steps of generating a dataset from a large clinical database, and to apply machine learning-based analytical techniques regarding.

Methods
Data of the Clinical Center of University of Debrecen was used. To structure the unstructured data, regular expressions and natural language processing methods were used. The main machine learning models were as follows: Gradient Boosting Machines to predict the risk of complication development; Long Short-Term Memory Networks to forecast future health outcomes. All analysis and procedures were done using Python.

Results
The database contains approximately 1600 tables, with a total size of 1.9 terabytes, where the largest table is 21.07 gigabytes with approximately 44.64 million rows. The final dataset consisted of 40,332 patients. Most variables originate from the unstructured data, including complications and comorbidities of diabetes, as well as physical and laboratory parameters. Related to laboratory parameters, the number of measurements and the median value for every half-year were available. The diagnosis time of the complications’ occurrence is also presented. Machine learning methods were more accurate compared with traditional statistical methods in predicting the prognosis (p &lt; 0.05).

Discussion
Our research highlights the importance of clinical data in chronic disease management. There are challenges in pre-processing and managing datasets, but machine learning-based methods are very efficient not only in extracting useful information from unstructured data but also in predicting the prognosis and identifying potential intervention points for better care.

Key messages
• Natural language processing can be used to obtain useful information from unstructured clinical data.
• Using machine learning techniques on clinical data could improve diabates care.
Integrating telemedicine in European healthcare settings: Key enablers for healthcare professionals
Authors: Schmitt T , Habimana K , Gottlob A , Habl C , Rosińska M , Sønderskov Frydensberg M , Anker Gents S , Leclercq V , Delnord M , Van den Bulcke M 
Published: October 2024

Abstract

Background
Conducted as part of WP4 (Sustainability) of the eCAN Joint Action (JA), we aimed to understand the facilitating factors for the use of telemedicine services in healthcare organisations (HCOs) involved in cancer care, such as comprehensive cancer centres and hospitals. eCAN JA runs pilot studies in 18 HCOs across 10 EU Member States (MS) to test the integration of telemedicine into the daily practice of healthcare professionals.

Methods
Based on a literature review, we identified 26 facilitators to implement telemedicine services at the organisational level; these were integrated to an online survey and sent to healthcare professionals working in cancer care. Next, they were invited to a foresight workshop to complement the quantitative survey data with qualitative insights.

Results
24 healthcare professionals across 12 EU MS, predominantly from the pilot sites of the eCAN JA, responded to the survey. They advocate for trainings on telemedicine, emphasising the importance of user-friendly and interoperable platforms, alongside sufficient technological support, time, and financial resources to effectively implement telemedicine in HCOs. Trial periods for both healthcare professionals and patients should offer first-hand experience, while harmonising telemedicine protocols ensure consistency. In addition, promoting digital literacy among physicians and providing IT education from an early age could facilitate adoption. These strategies, coupled with awareness campaigns and infrastructure investments in HCOs, can pave the way for telemedicine adoption.

Conclusions
Based on the quantitative and qualitative outcomes, it can be concluded that policies should establish telemedicine training for healthcare professionals, support interoperability and user-friendliness of these services in HCOs. They should also address hospital workload challenges, create harmonised protocols, provide incentives, and invest in digital infrastructure in HCOs for telemedicine adoption.

Key messages
• Telemedicine implementation in HCOs requires equipping healthcare professionals with the necessary skills, ensuring system compatibility, and addressing resource constraints in HCOs for success.
• Financial and infrastructural support to HCOs is crucial for the seamless integration of telemedicine services into the daily workflows of healthcare professionals, enhancing health service delivery.
Attitudes, fears and experiences of medical scientists towards using AI tools in their work routine
Authors: Fichtner U A , Knaus J , Graf E , Stelzer D , Sahlmann J , Wolkewitz M , Binder H , Weber S 
Published: October 2024

Abstract

Background
Artificial intelligence (AI) has experienced an explosive growth in the healthcare sector and the scientific field. Large Language Models (LLMs) are investigated as potential tool to be used in medical treatment, by e.g. producing doctor’s reports, supporting personalized medicine or educating patients. However, limitations are broadly discussed addressing patient safety and ethical concerns due to false and misleading content. Therefore, LLMs face criticism among users not only towards their integration in the patient-provider-interaction, but also in its use in medical research and health care education. In the ongoing EXPOLS (Exploring the Potential of LLMs in Statistical Consulting) study, we address the question ‘What are the attitudes, fears and experiences regarding the integration of AI tools in the clinical/scientific work routine?’

Methods
We implemented an online survey that will be accessible in 07/2024. All employees at the University and Medical Center Freiburg will be asked to participate in the survey via online media. We expect a sample size of n = 600-1,200. The survey combines published measurements (e.g. TRI 2.0, result expectation measure), as well as a self-designed instrument with 15 items aiming to measure fears and attitudes towards AI. Latter was developed within a multidisciplinary team making use of ChatGPT as item generator. We will investigate its psychometric properties using exploratory factor analysis and describe distribution characteristics.

Results
At the time of the conference, the project will be completed and both psychometric insights and content-related results will be reported. The results could have important public health implications, since attitudes towards AI will steer its integration in the medical and clinical routine.

Conclusions
The EXPOLS study makes a current inventory among medical scientists in Freiburg following a modern approach in instrument development by including ChatGPT as collegial advisor.

Key messages
• This study explores attitudes, fears and experiences towards the integration of AI tools in the work routine from medical and scientific staff at the University of Freiburg.
• The findings might be crucial in determining the integration of AI based tools in the medical and clinical work routine.