Search Results (20)

Introduction: Palliative needs assessment and referral to advanced palliative care resources are fundamental aspects of complex chronic patients’ care. Primary care Nurse Case Managers play a key role in the care of these patients. Objective: We aimed to describe the experiences of primary care Nurse Case Managers in palliative care needs identification and complex chronic patients’ referral to advanced palliative care resources. Method: This is a qualitative descriptive study with a phenomenological approach. Semi-structured online interviews were conducted with primary care Nurse Case Managers. A thematic analysis was performed using ATLAS.ti software. Results: 20 nurses participated, 16 of whom were women, with a mean age of 52.3 years and an average of 15.9 years of experience in primary care. Regarding “Palliative care Needs Assessment”, four sub-themes have been identified: “What do you understand?”, “How do you assess?”, “Difficulties” and “Alternatives” to current palliative care needs assessment. For the “Palliative Care Referral” theme four sub-themes have been identified: “Criteria”, “Tools”, “Difficulties” and “Alternatives” for referral. Discussion: Palliative needs are identified in patients with incurable diseases when there are no curative treatment options and when quality of life must be prioritized. Symptoms, general condition, progression, and comorbidity are assessed. Open interviews and home visits are essential for assessing the social and family context and the home resources available. Barriers identified include the conspiracy of silence, limited training in non-oncological palliative care, and a lack of staff and caregiver’s understanding of illness situation. The presence of difficult symptoms and a limited life expectancy were identified as key criteria for referral to palliative care. The physician’s assessment, the family’s request, and consultation with specialized teams play a key role in prognosis. Barriers include late referrals, lack of a palliative background, inequity in access to resources, and low visibility of the palliative care needs of non-cancer patients. Conclusions: Significant challenges remain in identifying palliative needs and referral to specialized resources, highlighting the need to optimize resources, strengthen professional training, and improve coordination between levels of care to ensure quality palliative care. Full article

Online adaptive radiotherapy (ART) is rapidly transforming clinical radiation oncology by enabling adaptation of treatment plans based on patient-specific anatomical and biological changes. However, most medical physics training programs lack structured education in ART. To address this critical gap, the Medical Physics Adaptive Radiotherapy (MPART) Fellowship was established at our center to train post-residency or practicing physicists in advanced adaptive technologies and workflows. The MPART Fellowship is a two-year program that provides immersive, platform-specific training in CBCT-guided (Varian Ethos), MR-guided (Elekta Unity), and PET-guided (RefleXion X1) radiotherapy. Fellows undergo modular clinical rotations, hands-on training, and dedicated research projects. The curriculum incorporates competencies in imaging, contouring, online planning, quality assurance, and team-based decision-making. Evaluation is based on the Accreditation Council for Graduate Medical Education competency domains and includes milestone tracking, mentor reviews, and structured presentations. The fellowship attracted applicants from both domestic and international institutions, reflecting strong demand for formal ART training. Out of 22 applications, two fellows have been successfully recruited into the program since 2024. Fellows actively participate in all phases of adaptive workflows and are expected to function at near-attending levels by the second year of their training. Each fellow also leads at least one translational or operational research project aimed at improving ART delivery. Fellows contribute to clinical coverage and lead developmental projects, resulting in presentations and publications at the national and international levels. The MPART Fellowship addresses a vital educational need by equipping medical physicists with the advanced competencies necessary for implementing and leading ART. This program offers a replicable framework for other institutions seeking to advance precision radiation therapy through structured post-residency training in adaptive radiotherapy. As this fellowship program is still in its early phase of establishment, the primary goal of this paper is to introduce the structure, framework, and implementation model of the program. Comprehensive outcome analyses—such as quantitative assessments, fellow feedback, and longitudinal competency evaluations—will be incorporated in future work as additional cohorts complete training. Full article

Adult learner dropout is adults’ withdrawal or stop-out from formal or non-formal educational programs before successful completion. For adult learners, withdrawal often manifests as stop-out or temporary disengagement rather than permanent attrition, reflecting the episodic nature of their participation. Unlike traditional students, adult learners must often balance multiple life responsibilities—employment, caregiving, financial obligations, and community roles—while also pursuing education or training. Their vulnerability to attrition is further exacerbated by these overlapping demands, particularly when educational programs do not accommodate their situational and motivational needs. Adult learner dropout therefore requires a more dynamic understanding of persistence as a continuous negotiation between internal and external demands. Participation in online education has significantly expanded over the past two decades, particularly during and after the COVID-19 pandemic, as adult learners increasingly engage with digital platforms for work and communication. This exposure has enhanced their digital fluency, transforming their expectations and experiences of online learning. Thus, the underlying factors that influence adult learner dropout have also shifted—moving beyond technological and access-related barriers to instructional quality, engagement design, and relevance issues. In this evolving landscape, adult learner dropout can no longer be regarded as isolated or individual events. It is a systemic phenomenon emerging from dynamic interactions among psychological, pedagogical, contextual, and institutional factors. Full article

Background: Population ageing is a global challenge, prompting ageing-in-place policies in Hong Kong to support community-dwelling older adults while reducing healthcare costs. Yet, their impact on health-related quality of life (HRQoL) remains underexplored amid Hong Kong’s long life expectancy and growing older population. Traditional surveys are costly and time-consuming, while routinely collected registration data offers a large, efficient source for health insights. This study uses enhanced administrative data to track HRQoL trajectories and inform policy. Methods: This is a prospective, open-ended longitudinal study, enrolling adults aged 50 or older from a collaborating non-governmental organization in Hong Kong’s Southern District. Data collection, started in February 2021, occurs annually via phone and face-to-face interviews by trained social workers and volunteers using a standardized questionnaire to assess individual (e.g., socio-demographics), environmental (e.g., social support via Lubben Social Network Scale-6), biological (e.g., chronic illnesses), functional (e.g., cognition via Montreal Cognitive Assessment), and HRQoL (e.g., EQ-5D-5L) factors. A secure online system links health and service use data (e.g., service utilization like community care visits). Analysis employs descriptive statistics, group comparisons, correlations, growth modelling to identify health trajectories, and structural equation modelling to test a revised quality-of-life framework. Sample size (projected 470–580 after two follow-ups from a 2321 baseline) is based on power calculations: 300–500 for latent class growth analysis (LCGA) class detection and 200–400 for structural equation modelling (SEM) fit (e.g., RMSEA < 0.06) at 80% power/α = 0.05, simulated via Monte Carlo with a 50–55% attrition. Discussion: This is the first longitudinal HRQoL study in Hong Kong using enhanced non-governmental organization (NGO) administrative data, integrating social–ecological and HRQoL models to predict trajectories (e.g., stable vs. declining mobility) and project care demands (e.g., increase in in-home care for frailty). Unlike prior cross-sectional or inpatient studies, it offers a scalable model for NGOs, informing ageing-in-place policy effectiveness and equitable geriatric care. Full article

Objective: The purpose of this study was to investigate the knowledge, attitudes, and practices (KAPs), including ageism, of rehabilitation service providers regarding older adults with substance use disorders to examine the association between KAPs and ageism on the knowledge of rehabilitation providers and confidence in treating this population. Methods: An online survey was developed to assess providers’ familiarity with geriatric substance use disorders, attitudes towards aging, and perceived barriers to treatment. The survey included the Expectations Regarding Aging (ERA-12) tool to measure ageist attitudes. Data was collected from 25 rehabilitation healthcare providers across rehabilitation centers in North Carolina. Descriptive statistics and ERA-12 scoring were used to analyze the results. Results: Most (52.0%) respondents reported slight or moderate familiarity with specific risk factors for substance use disorders associated with older adults, and participants most commonly expressed ambivalence (48.0% indicated they were neither satisfied nor dissatisfied) with their training on this demographic. Barriers included a lack of specialized training, limited availability of age-appropriate treatment programs, and resistance to change. Negative attitudes towards aging and substance use disorders were prevalent among respondents. Providers indicated a need for enhanced education, clinical guidelines, and access to geriatric-trained professionals. Discussion: The findings highlight a critical need for specialized training for rehabilitation providers to improve care for older adults with substance use disorders. Addressing ageism, increasing awareness, and enhancing provider education are essential to improving treatment outcomes. Implementing targeted training programs and specialized resources could significantly enhance the quality of care for this underserved population. Full article

Background: Progress in medical education is reflected in healthcare quality and patient satisfaction. However, there are still gaps in knowledge regarding the patients’ expectations of general practitioners (GPs), even though patients’ interactions with GPs are among the most frequent in healthcare. Accordingly, this study investigated the qualities desired by adults in GPs, intending to include them in future medical humanization education to enhance the patients’ experience. Methods: Using an online survey, 1057 respondents (52.8% female; mean age, 46.55 years old, SD 16.03) were asked to rate 15 statements on a scale of 1 to 10 regarding desirable characteristics of general practitioners. In addition to the respondents’ high regard for their GP’s ongoing education and sincerity in conveying both positive and negative information, the findings suggest that the socio-demographic attributes of the respondents influence their expectations of GPs. Results: Women had higher overall expectations than men. In addition, those who reported higher satisfaction with their lives and recent healthcare users had higher expectations of GPs than those who were dissatisfied. The youngest group of respondents exhibited the lowest expectations of the GPs’ attributes, except for their greater willingness to engage in discussions with GPs regarding personal or professional issues, compared with the older groups. Conclusions: The information presented in this work can be utilized during medical students’ humanization training and physicians’ continuing education. The knowledge gained should enhance GPs’ awareness of the distinctions in patients’ expectations, enabling them to adapt their approach and services to align with their patients’ unique needs, expectations, and experiences. Full article

Pain continues to be a significant problem for cancer patients, and the impact of a population-based strategy on their experiences is not completely understood. Our study aimed to determine the impact of palliative care on mitigating pain and its associated effects in determining the quality of life (QoL) among colon cancer outpatients. Six collection databases were used to perform a structured systematic review of the available literature, considering all papers published between the year 2000 and February 2023. PRISMA guidelines were adopted in our study, and a total of 9792 papers were evaluated. However, only 126 articles met the inclusion criteria. A precise diagnosis of disruptive colorectal cancer (CRC) pain disorders among patients under palliative care is necessary to mitigate it and its associated effects, enhance health, promote life expectancy, increase therapeutic responsiveness, and decrease comorbidity complications. Physical activities, the use of validated pain assessment tools, remote outpatient education and monitoring, chemotherapeutic pain reduction strategies, music and massage therapies, and bridging social isolation gaps are essential in enhancing QoL. We recommend and place a strong emphasis on the adoption of online training/or coaching programs and the integration of formal and informal palliative care systems for maximum QoL benefits among CRC outpatients. Full article

In line with United Nations Sustainable Development Goal (SDG) 4.2, South Africa’s National Development Plan commits to providing high-quality early childhood education to all children by 2030 to drive improved child outcomes. Prior to 2016, South Africa lacked reliable, locally standardised, valid, and cross-culturally fair assessment tools for measuring preschool quality and child outcomes, suitable for use at scale within a resource-constrained context. In this paper we detail the development and evolution of a suite of early learning measurement (ELOM) tools designed to address this measurement gap. The development process included reviews of literature and other relevant assessment tools; a review of local curriculum standards and expected child outcomes; extensive consultation with government officials, child development experts, and early learning practitioners, iterative user testing; and assessment of linguistic, cultural, functional, and metric equivalence across all 11 official South African languages. To support use of the ELOM tools at scale, and by users with varying levels of research expertise, administration is digitised and embedded within an end-to-end data value chain. ELOM data collected since 2016 quantify the striking socio-economic gradient in early childhood development in South Africa, demonstrate the relationship between physical stunting, socio-emotional functioning and learning outcomes, and provide evidence of the positive impact of high-quality early learning programmes on preschool child outcomes. To promote secondary analyses, data from multiple studies are regularly collated into a shared dataset, which is made open access via an online data portal. We describe the services and support that make up the ELOM data value chain, noting several key challenges and enablers of data-driven change within this context. These include deep technical expertise within a multidisciplinary and collaborative team, patient and flexible capital from mission-aligned investors, a fit-for-purpose institutional home, the appropriate use of technology, a user-centred approach to development and testing, sensitivity to children’s diverse linguistic and socio-economic circumstances, careful consideration of requirements for scale, appropriate training and support for a non-professional assessor base, and a commitment to ongoing learning and continuous enhancement. Practical examples are provided of ways in which the ELOM tools and data are used for programme monitoring and enhancement purposes, to evaluate the relative effectiveness of early learning interventions, to motivate for greater budget and inform more effective resource allocation, to support the development of enabling Government systems, and to track progress towards the attainment of national and global development goals. We share lessons learnt during the development of the tools and discuss the factors that have driven their uptake in South Africa. Full article

RUL (remaining useful life) estimation is one of the main functions of the predictive analytics systems for rotary machines. Data-driven models based on large amounts of multisensory measurements data are usually utilized for this purpose. The use of adjustable bearings, on the one hand, improves a machine’s performance. On the other hand, it requires considering the additional variability in the bearing parameters in order to obtain adequate RUL estimates. The present study proposes a hybrid approach to such prediction models involving the joint use of physics-based models of adjustable bearings and data-driven models for fast on-line prediction of their parameters. The approach provides a rather simple way of considering the variability of the properties caused by the control systems. It has been tested on highly loaded locomotive traction motor axle bearings for consideration and prediction of their wear and RUL. The proposed adjustable design of the bearings includes temperature control, resulting in an increase in their expected service life. The initial study of the system was implemented with a physics-based model using Archard’s law and Reynolds equation and considering load and thermal factors for wear rate calculation. The dataset generated by this model is used to train an ANN for high-speed on-line bearing RUL and wear prediction. The results show good qualitative and quantitative agreement with the statistics of operation of traction motor axle bearings. A number of recommendations for further improving the quality of predicting the parameters of active bearings are also made as a summary of the work. Full article

Young dementia carers need to be recognised and supported in their role. They need help to understand the illness, what changes are expected and how it can affect their family member. Many support services, partly due to the COVID pandemic, have moved online and have been shown to be acceptable as they are low cost and reduce access barriers. iSupport is an evidence-informed e-health training programme developed by the World Health Organization (WHO) to support adult dementia carers. This paper reports on the co-design of an adapted version of iSupport for young carers. A theoretically driven co-design approach, drawing on the lived experiences of young dementia carers and experts who work with this target group was followed. As a result of this study iSupport for Young Carers was created. It is the first e-health intervention of its kind and aims to support the mental health, knowledge and skills of young dementia carers. In turn, it could improve the quality of the support that service providers can offer, and this can result in increased levels of identification of these young people. The work presented also provides opportunities for other countries and demographic groups to translate and adapt iSupport for Young Carers to their specific cultural context. Full article

Background: The number of people living with dementia (PwD) worldwide is expected to double every 20 years. Many continue living at home, receiving support from family caregivers who may experience significant stress, simultaneously to that of the PwD. Meaningful and effective home-based interventions to support PwD and their caregivers are needed. The development of a theory- and practice-driven online home-based music intervention (MI) is delivered by credentialed music therapists, nested within the HOMESIDE RCT trial. Methods: Dyads including the PwD and their family carer are randomised to MI, reading (RI) or standard care (SC). MI aims to support health wellbeing and quality of life by training caregivers to intentionally use music (singing, instrument playing, movement/dancing, and music listening) with their family member (PwD) in daily routines. MI is underpinned by cognitive, relational, social, and psychological theories of mechanisms of change. Results: Preliminary sub-cohort results analyses show MI can be delivered and is accepted well by participants and music-therapist interventionists across five countries. Conclusions: The specialist skills of a music therapist through MI enable carers to access music when music therapists are not present, to meet carer and PwD needs. Music therapists embrace this changing professional role, observing therapeutic change for members of the dyads. Full article

To protect our planet, the material recycling of domestic waste is necessary. Since the COVID-19 pandemic began, the volume of domestic waste has surged overwhelmingly, and many countries suffered from poor waste management. Increased demand for food delivery and online shopping led to a huge surge in plastic and paper waste which came from natural resources. To reduce the consumption of resources and protect the environment from pollution, such as that from landfills, waste should be recycled. One of precious recyclable materials from household waste is liquid cartons that are made of high-quality paper. To promote sustainable recycling, this paper proposes a vision-based inspection module based on convolutional neural networks via transfer learning (CNN-TL) for collecting liquid packaging cartons in the reverse vending machine (RVM). The RVM is an unmanned automatic waste collector, and thus it needs the intelligence to inspect whether a deposited item is acceptable or not. The whole processing algorithm for collecting cartons, including the inspection step, is presented. When the waste is inserted into the RVM by a user after scanning the barcode on the waste, it is relocated to the inspection module, and the item is weighed. To develop the inspector, an experimental set-up with a video camera was built for image data generation and preparation. Using the image data, the inspection agent was trained. To make a good selection for the model, 17 pretrained CNN models were evaluated, and DenseNet121 was selected. To access the performance of the cameras, four different types were also evaluated. With the same CNN model, this paper found the effect of the number of training epochs being set to 10, 100, and 500. In the results, the most accurate agent was the 500-epoch model, as expected. By using the RVM process logic with this model, the results showed that the accuracy of detection was over 99% (overall probability from three inspections), and the time to inspect one item was less than 2 s. In conclusion, the proposed model was verified for whether it would be applicable to the RVM, as it could distinguish liquid cartons from other types of paper waste. Full article

During the COVID-19 pandemic, as offline learning activities were blocked, teachers’ training activities were moved from face-to-face to online training. Therefore, teachers had to join an increasing number of online training sessions. However, few studies have focused on teachers’ satisfaction with online training. To address this gap, based on the American user satisfaction theory model (ACSI), this study established the factors of expectation of online training quality, perceived online training quality, perceived online training value, and teacher satisfaction with online learning, and aimed to explore their relationships with six hypotheses. A total of 397 middle school teachers who had online training experience participated in the survey through an online questionnaire. SPSS 26.0 and AMOS 23.0 were used to analyze the data. The results showed that (1) expectation of online training quality was positively correlated with perceived online training quality; (2) expectation of online training quality was negatively correlated with perceived online training value; (3) perceived online training quality was positively correlated with perceived online training value; and (4) perceived online training value was positively correlated with online training satisfaction. The findings imply that teachers should be informed in advance of various difficulties that may be encountered in online training, so as to reduce their expectations of online training quality. In addition, in order to improve teachers’ perceived quality and perceived value of online training, intervention strategies should be proposed, online training platforms should be optimized, and online training methods should be innovated to improve teachers’ sustainable development ability. Full article

This article presents the concept of federated learning (FL) of eXplainable Artificial Intelligence (XAI) models as an enabling technology in advanced 5G towards 6G systems and discusses its applicability to the automated vehicle networking use case. Although the FL of neural networks has been widely investigated exploiting variants of stochastic gradient descent as the optimization method, it has not yet been adequately studied in the context of inherently explainable models. On the one side, XAI permits improving user experience of the offered communication services by helping end users trust (by design) that in-network AI functionality issues appropriate action recommendations. On the other side, FL ensures security and privacy of both vehicular and user data across the whole system. These desiderata are often ignored in existing AI-based solutions for wireless network planning, design and operation. In this perspective, the article provides a detailed description of relevant 6G use cases, with a focus on vehicle-to-everything (V2X) environments: we describe a framework to evaluate the proposed approach involving online training based on real data from live networks. FL of XAI models is expected to bring benefits as a methodology for achieving seamless availability of decentralized, lightweight and communication efficient intelligence. Impacts of the proposed approach (including standardization perspectives) consist in a better trustworthiness of operations, e.g., via explainability of quality of experience (QoE) predictions, along with security and privacy-preserving management of data from sensors, terminals, users and applications. Full article

Educating and raising awareness among lay members of the public about palliative care can significantly improve the care for terminally ill patients and their quality of life. This paper reports on the survey aimed at assessing the experience and expectations of participants in the Last Aid course launched in Slovenia in 2019 to train hospice volunteers and promote dialogue on death and dying. The course implementation was supported by materials prepared, translated, and/or adapted from German under the PO-LAST project, which linked Slovenian medical and healthcare professionals, hospice representatives, and university students. The Last Aid course follows an international four-module curriculum that has been successfully applied in 18 countries so far. In Slovenia, the course was delivered 30 times with 21 in-person deliveries and 9 online events attended by 450 participants of different sexes, ages, and professions. The surveyed population included 250 people who returned the evaluation questionnaires by October 2020. The aim of the analysis was to gain insight that can be applied broadly in future work and research on adult education on palliative care and the erasure of death-related taboos. Full article