Search Results (551)

Background: Family caregivers are a vital yet often under-recognized part of the healthcare system. They provide essential emotional, physical, and logistical support to individuals with illness, disability, or frailty, and their contributions improve continuity of care and reduce system strain. However, many healthcare and social service providers are not equipped to meaningfully engage caregivers as partners. In Alberta, stakeholders validated the Caregiver-Centered Care Competency Framework and identified the need for a three-tiered education model—Foundational, Advanced, and Champion—to help providers recognize, include, and support family caregivers across care settings. This paper focuses on the development and early evaluation of the Advanced Caregiver-Centered Care Education modules, designed to enhance the knowledge and skills of providers with more experience working with family caregivers. The modules emphasize how partnering with caregivers benefits not only the person receiving care but also improves provider effectiveness and supports better system outcomes. Methods: The modules were co-designed with a 154-member interdisciplinary team and grounded in the competency framework. Evaluation used the first three levels of the Kirkpatrick–Barr health workforce education model. We analyzed pre- and post-surveys from the first 50 learners in each module using paired t-tests and examined qualitative feedback and SMART goals through inductive content analysis. Results: Learners reported a high level of satisfaction with the education delivery and the knowledge and skill acquisition. Statistically significant improvements were observed in 53 of 54 pre-post items. SMART goals reflected intended practice changes across all six competency domains, indicating learners saw value in engaging caregivers as partners. Conclusions: The Advanced Caregiver-Centered Care education improved providers’ confidence, knowledge, and skills to work in partnership with family caregivers. Future research will explore whether these improvements translate into real-world practice changes and better caregiver experiences in care planning, communication, and navigation. Full article

Background/Objectives: There is a growing need for autism spectrum disorder (ASD) assessment tools that are diagnostically aligned, clinically usable, and accessible across diverse service contexts. The Diagnostic Autism Spectrum Interview—Version 2 (DASI-2) is a freely available, semi-structured clinical interview mapped directly to DSM-5 and ICD-11 criteria. This pilot study aimed to adapt DASI-2 into Hungarian and explore the (1) acceptability of DASI-2 administration, (2) agreement with prior clinical ASD diagnoses, and (3) relationship between DASI-2 observational ratings and ADOS-2 classifications. Methods: Following a multistep translation procedure, DASI-2 was administered to seven children previously assessed for ASD in a multidisciplinary Hungarian clinical setting. The assessment included a parent interview, direct assessment with the child or young person, and completion of the DASI observational record (OR1–OR4). DASI diagnostic outcomes were compared with prior clinical decisions, and OR scores were analyzed in relation to ADOS-2 classifications. Results: All participants completed the DASI-2 interview in full. Agreement with prior clinical diagnosis was found in six of seven cases (κ = 0.70, indicating substantial agreement). When exploring the one non-aligned case, the divergence in diagnostic outcome was due to broader contextual information considered by the initial clinical team which influenced clinical opinion. The five participants diagnosed with ASD showed substantially higher DASI observational scores (mean = 15.26) than the two who were not diagnosed (mean = 1.57), mirroring ADOS-2 severity classifications. Conclusions: These findings support the acceptability and preliminary validity of DASI-2. Its inclusive structured observational record may provide a practical complement to resource-intensive tools such as the ADOS-2; however, further validation in larger and more diverse samples is needed. Full article

The present study aimed to provide Congolese refugee women with an opportunity to narrate firsthand experiences coping with resettlement challenges in the United States. Translator-assisted, face-to-face semi-structured individual interviews were conducted with newly resettled Congolese refugee women (n = 20) aged 18 and older who arrived in the United States between 2011 and 2018. All participants were receiving assistance from a resettlement agency, located in the Midwestern US, at the time of the study. Data were analyzed using descriptive coding and thematic analysis. Three overarching themes were developed, indicating that Congolese refugee women adopt three main coping mechanisms to deal with challenges they face after resettling in the United States: (1) use of social support, (2) acceptance of the situation, and (3) spirituality. Resettlement support services, such as those provided by resettlement agencies, mental health providers, and community-based organizations, should integrate both economic and cultural dimensions into their services to address the complex physiological, mental, and emotional impacts of resettlement. These services should prioritize culturally and spiritually sensitive techniques that are linguistically accessible. Full article

Background/Objectives: BRIGHTLIGHT was the national evaluation of adolescent and young adult (AYA) cancer services in England. BRIGHTLIGHT results were not available when the most recent healthcare policy (NHSE service specifications for AYA Cancer) for AYA was drafted and therefore did not consider BRIGHTLIGHT findings and recommendations. We describe the co-development and delivery of a Policy Lab to expedite the implementation of the new service specification in the context of BRIGHTLIGHT results, examining the roles of multi-stakeholders to ensure service delivery is optimised to benefit AYA patients. We address the key question, “What is the roadmap for empowering different stakeholders to shape how the AYA service specifications are implemented?”. Methods: A 1-day face-to-face policy lab was facilitated, utilising a unique, user-centric engagement approach by bringing diverse AYA stakeholders together to co-design strategies to translate BRIGHTLIGHT evidence into policy and impact. This was accompanied by an online workshop and prioritisation survey, individual interviews, and an AYA patient workshop. Workshop outputs were analysed thematically and survey data quantitatively. Results: Eighteen professionals and five AYAs attended the face-to-face Policy Lab, 16 surveys were completed, 13 attended the online workshop, three professionals were interviewed, and three AYAs attended the patient workshop. The Policy Lab generated eight national and six local recommendations, which were prioritised into three national priorities: 1. Launching the service specification supported by compelling communication; 2. Harnessing the ideas of young people; and 3. Evaluation of AYA patient outcomes/experiences and establishing a national dashboard of AYA cancer network performance. An animation was created by AYAs to inform local hospitals what matters to them most in the service specification. Conclusions: Policy and research evidence are not always aligned, so when emerging evidence does not support current guidance, further exploration is required. We have shown through multi-stakeholder involvement including young people that it was possible to gain a different interpretation based on current knowledge and context. This additional insight enabled practical recommendations to be identified to support the implementation of the service specification. Full article

Pharmacist-led transitions of care (TOC) services decrease preventable hospital readmission. TOC service implementation assessment can inform translation to real-world settings. The purpose of this study was to evaluate the implementation of a TOC service for patients with multiple admissions at a regional hospital using the RE-AIM framework. In this quasi-experimental, non-randomized study, individuals with ≥2 recent hospitalizations received pharmacist-led discharge medication reconciliation and counseling, management of drug-related problems, post-discharge telephonic visits, and social support. The reach, effectiveness, implementation, and maintenance RE-AIM dimensions were assessed using patient and service records. Outcomes included 30-day readmission rates for individuals completing ≥1 outpatient pharmacist visit (intervention) versus those unreachable in the outpatient setting (comparison), completed interventions, implementation features, and service adaptations. Chi-square and Fisher’s exact tests were used for comparison of categorical variables and the t-test was used for continuous variables. From February 2022 to August 2023, 72.7% of the 66 service participants participated in the intervention (reach). Additionally, 30-day readmission was 22.9% (intervention) versus 55.6% (comparison; p = 0.01). In total, 2279 interventions were documented (effectiveness). The service was adapted (implementation) and expanded to include additional populations (maintenance) to enhance sustainability. Based on RE-AIM evaluation, the pharmacist-led TOC intervention appears to be a sustainable solution for addressing readmission in multivisit patients. Full article

The transformer-based deep learning approach represents the current state-of-the-art in machine translation (MT) research. Large-scale pretrained transformer models produce state-of-the-art performance across a wide range of MT tasks for many languages. However, such deep neural network (NN) models are often data-, compute-, space-, power-, and energy-hungry, typically requiring powerful GPUs or large-scale clusters to train and deploy. As a result, they are often regarded as “non-green” and “unsustainable” technologies. Distilling knowledge from large deep NN models (teachers) to smaller NN models (students) is a widely adopted sustainable development approach in MT as well as in broader areas of natural language processing (NLP), including speech, and image processing. However, distilling large pretrained models presents several challenges. First, increased training time and cost that scales with the volume of data used for training a student model. This could pose a challenge for translation service providers (TSPs), as they may have limited budgets for training. Moreover, CO₂ emissions generated during model training are typically proportional to the amount of data used, contributing to environmental harm. Second, when querying teacher models, including encoder–decoder models such as NLLB, the translations they produce for low-resource languages may be noisy or of low quality. This can undermine sequence-level knowledge distillation (SKD), as student models may inherit and reinforce errors from inaccurate labels. In this study, the teacher model’s confidence estimation is employed to filter those instances from the distilled training data for which the teacher exhibits low confidence. We tested our methods on a low-resource Urdu-to-English translation task operating within a constrained training budget in an industrial translation setting. Our findings show that confidence estimation-based filtering can significantly reduce the cost and CO₂ emissions associated with training a student model without drop in translation quality, making it a practical and environmentally sustainable solution for the TSPs. Full article

Accurate short-term precipitation forecasting (nowcasting) based on weather radar data is essential for managing weather-related risks, particularly in applications such as airport operations, urban flood prevention, and public safety during outdoor events. This study proposes a computationally efficient nowcasting method based on a Lagrangian advection scheme, estimating both the translation and rotation of radar-observed precipitation fields without relying on machine learning or resource-intensive computation. The method was tested on a two-year dataset (2022–2023) over Tuscany, using data collected from the Italian Civil Protection Department’s radar network. Forecast performance was evaluated using the Critical Success Index (CSI) and Mean Absolute Error (MAE) across varying spatial domains (1° × 1° to 2° × 2°) and precipitation regimes. The results show that, for high-intensity events (average rate > 1 mm/h), the method achieved CSI scores exceeding 0.5 for lead times up to 2 h. In the case of low-intensity rainfall (average rate < 0.3 mm/h), its forecasting skill dropped after 20–30 min. Forecast accuracy was shown to be highly sensitive to the temporal stability of precipitation intensity. The method performed well under quasi-stationary stratiform conditions, whereas its skill declined during rapidly evolving convective events. The method has low computational requirements, with forecasts generated in under one minute on standard hardware, and it is well suited for real-time application in regional meteorological centres. Overall, the findings highlight the method’s effective balance between simplicity and performance, making it a practical and scalable option for operational nowcasting in settings with limited computational capacity. Its deployment is currently being planned at the LaMMA Consortium, the official meteorological service of Tuscany. Full article

Tourist satisfaction models typically assume that service performance dimensions carry the same weight for all travelers. Drawing on Bourdieu, we reconceptualize age, gender, and region of origin as demographic capital, durable resources that mediate how visitors decode service cues. Using a SERVPERF-based survey of 407 international travelers departing Quito (Ecuador), we test measurement invariance across six sociodemographic strata with multi-group confirmatory factor analysis. The four-factor SERVPERF core (Access, Lodging, Extra-hotel Services, Attractions) holds, yet partial metric invariance emerges: specific loadings flex with demographic capital. Gen-Z travelers penalize transport reliability and safety; female visitors reward cleanliness and empathy; and Latin American guests are the most critical of basic organization. These patterns expose a boundary condition for universalistic satisfaction models and elevate demographic capital from a descriptive tag to a structuring construct. Managerially, we translate the findings into segment-sensitive levers, visible security for youth and regional markets, gender-responsive facility upgrades, and dual eco-luxury versus digital-detox bundles for long-haul segments. By demonstrating when and how SERVPERF fractures across sociodemographic lines, this study intervenes in three theoretical conversations: (1) capital-based readings of consumption, (2) the search for boundary conditions in service-quality measurement, and (3) the shift from segmentation to capital-sensitive interpretation in emerging markets. The results position Ecuador as a critical case and provide a template for destinations facing similar performance–perception mismatches in the Global South. Full article

Amid growing environmental concerns and an increasing push for sustainable development, countries in the Middle East and North Africa (MENA) region have taken proactive steps toward green growth, energy transition, and technological innovation. As a result, this study examines the effects of green growth, energy transition, technological innovation, financial inclusion, and urbanization on environmental sustainability in the Middle East and North Africa (MENA) region. Moreover, this study breaks new ground by exposing the hidden environmental costs of financial inclusion, urbanization, and technological innovation in the MENA region’s development trajectory, thereby providing compelling evidence for rethinking sustainability through an integrated approach that aligns economic ambition with ecological responsibility. Data for the studied variables were sourced from the World Bank database covering the period 1990 to 2021. The results show that green growth and energy transition significantly reduce CO₂ emissions, supporting the idea that economic expansion aligned with environmental priorities can contribute to ecological improvement. However, the impact of technological innovation is statistically insignificant, indicating that innovation in the region has not yet translated into meaningful environmental gains, possibly due to the dominance of non-green or industrial-focused innovation. Financial inclusion is found to increase CO₂ emissions, likely by facilitating greater access to credit and financial services that fuel energy-intensive consumption and production activities. Similarly, urbanization also contributes to rising emissions, reflecting the unsustainable nature of urban growth in many MENA region. Based on this study, we advocate for a coordinated regional approach to climate and energy policy, underpinned by shared governance and collective action. Full article

In the context of the rapid digital transformation of the catering industry, the design of ordering interfaces—key hubs of human–computer interaction—has become critical to user service quality and brand competitiveness, especially in terms of usability, visual appeal, and emotional resonance. Based on a human–computer interaction design framework, this study proposes a dual-path optimization model integrating user demand mapping and conflict resolution to synergize explicit need translation with innovative design problem solving. The model employs KE to capture implicit user needs, applies AHP to construct a weighted design element system, and uses QFD to establish a matrix linking user needs with technical attributes. To address contradictions among design elements, TRIZ is introduced to resolve conflicts between functional redundancy and interaction efficiency. Additionally, generative AI tools such as MidJourney are incorporated to accelerate concept generation and improve innovation. Based on user evaluations and controlled experiments, the optimized design demonstrates measurable improvements in task efficiency and visual appeal. Overall, the dual-path approach effectively bridges the gap between vague user needs and concrete design solutions, achieving a balanced integration of functionality, aesthetics, and interactivity. The proposed model overcomes the limitations of experience-driven design by offering a systematic methodology encompassing demand analysis, technological transformation, conflict resolution, and intelligent generation, with practical value for enhancing the user experience of digital service touchpoints in the catering sector. Full article

This study examines whether China’s rapid spread of internet and mobile information technologies has translated into greater household support for government air-quality programs. Using nationally representative data from the Chinese General Social Survey (2018), this study estimates the causal impact of digital media use on residents’ willing to pay (WTP) each month for one additional “good-air” day. Ordinary least squares shows that individuals who rely primarily on the internet or mobile push services are willing to contribute CNY 1.9–2.7 more—about 43 percent above the sample mean of CNY 4.41. To address potential endogeneity, we instrumented digital media adoption using provincial computer penetration; two-stage least squares yielded roughly CNY 10.5, confirming a causal effect. Mechanism tests showed that digital access lowers complacency about local air quality, strengthens anthropogenic attribution of pollution, and heightens the moral norm that economic sacrifice is legitimate, jointly mediating the rise in WTP. Heterogeneity analyses revealed stronger effects among high-income households and renters, while extended tests showed that (i) the impact intensifies when the promised environmental gain rises from one to three or five clean-air days, (ii) attention to international news can crowd out local WTP, and (iii) digital media raise not only the likelihood of paying but also the amount paid among existing contributors. The findings suggest that targeted digital outreach—especially messages with concrete, locally salient goals—can substantially enlarge the fiscal base for air-quality initiatives, helping China advance its ecological-civilization and dual-carbon objectives. Full article

Introduction: Few applications worldwide focus on psychosocial rehabilitation, and none specifically address psychosocial rehabilitation projects. This justifies the need for an application to assist mental health professionals in constructing and managing such projects in the Brazilian mental health scenario. Objective: This study aimed to present a web application, the “Psychosocial Rehabilitation Application” (Psychosocial Rehab App), and describe its development in detail through a technological survey conducted between May 2024 and February 2025. Method: The development process of the web app was carried out in the following four stages, adapted from the Novak method: theoretical basis, requirements survey, prototyping, and development with alpha testing. The active and collaborative participation of the main researcher (a psychiatric nurse) and two undergraduate software engineers, supervised by a software engineer and a professor of nursing and psychology, was essential for producing a suitable operational product available to mental health professionals. Interactions were conducted via video calls, WhatsApp, and email. These interactions were transcribed using the Transkriptor software and inserted into the ATLAS.ti software for thematic analysis. Results: The web app “Psychosocial Rehabilitation Application” displays a home screen for registration and other screens structured into the stages of the psychosocial rehabilitation project (assessment, diagnosis, goals, intervention, agreements, and re-assessment). It also has a home screen, a resource screen, and a function screen with options to add a new project, search for a project, or search for mental health support services. These features facilitate the operation and streamline psychosocial rehabilitation projects by mental health professionals. Thematic analysis revealed three themes and seven codes describing the entire development process and interactions among participants in collaborative, interrelational work. A collaborative approach between researchers and developers was essential for translating the complexity of the psychosocial rehabilitation project into practical and usable functionalities for future users, who will be mental health professionals. Discussion: The Psychosocial Rehab App was developed collaboratively by mental health professionals and developers. It supports the creation of structured rehabilitation projects, improving decision-making and documentation. Designed for clinical use, the app promotes autonomy and recovery by aligning technology with psychosocial rehabilitation theory and the actual needs of mental health services. Conclusions: The Psychosocial Rehab App was developed through collaborative work between mental health and technology professionals. The lead researcher mediated this process to ensure that the app’s functionalities reflected both technical feasibility and therapeutic goals. Empathy and dialog were key to translating complex clinical needs into usable and context-appropriate technological solutions. Full article

Abiotic stressors threaten honeybee health, jeopardizing pollination services critical to agriculture and biodiversity. Here, we identified the AmVgR gene, which encodes a member of the low-density lipoprotein receptor family, and examined its function in the response of Apis mellifera to adverse abiotic stress. AmVgR exhibited peak expression in adult workers and was significantly upregulated under heat, cold, heavy metal, and pesticide exposure. RNAi-mediated knockdown of AmVgR suppressed antioxidant enzyme activities, elevated the levels of oxidative damage markers, and downregulated antioxidant gene expression. Crucially, AmVgR silencing reduced survival under H₂O₂-induced oxidative stress, indicating its essential role in stress resilience. Our findings highlight AmVgR as a key regulator of antioxidant defense during development and environmental adaptation in Apis mellifera. This study provides mechanistic insights into bee stress physiology and proposes AmVgR as a novel target for enhancing pollinator protection strategies. Further research should elucidate its molecular pathways and translational applications in mitigating abiotic stress impacts. Full article

Background/Objectives: Child abuse is a pervasive global issue with significant implications for the physical, emotional, and psychological well-being of victims. This review highlights the clinical, molecular, and therapeutic dimensions of child abuse, emphasizing its long-term impact and the need for interdisciplinary approaches. Early exposure to abuse activates the hypothalamic-pituitary-adrenal (HPA) axis, leading to chronic cortisol release and subsequent neuroplastic changes in brain regions such as the hippocampus, amygdala, and prefrontal cortex. These molecular alterations, including epigenetic modifications and inflammatory responses, contribute to the heightened risk of psychiatric disorders and chronic illnesses in survivors. Clinically, child abuse presents with diverse manifestations ranging from physical injuries to psychological and developmental disorders, making timely diagnosis challenging. Methods: A multidisciplinary approach involving thorough clinical evaluation, detailed histories, and collaboration with child protection services is essential for accurate diagnosis and effective intervention. Results: Recent advances in molecular biology have identified biomarkers, such as stress-related hormones and epigenetic changes, which provide novel insights into the physiological impact of abuse and potential targets for therapeutic intervention. Current treatment strategies prioritize the child’s safety, psychological well-being, and prevention of further abuse. Trauma-focused cognitive behavioral therapy and family-centered interventions are pivotal in promoting recovery and resilience. Conclusions: Emerging research focuses on integrating molecular findings with clinical practice, utilizing digital health tools, and leveraging big data to develop predictive models and personalized treatments. Interdisciplinary collaboration remains crucial to translating research into policy and practice, ultimately aiming to mitigate the impact of child abuse and improve outcomes for survivors. Full article

Background/Objectives: Organizing nurses’ work is crucial for ensuring patient care quality and efficiency. Nurses’ work methods directly influence patient safety and healthcare outcomes, making them vital for effective health services. Assessing these methods helps identify effective practices, enhance work organization, and improve both professional satisfaction and patient safety. This study aims to translate, adapt, and validate the Nurse Work Method Assessment Scale (NWMAS) for Turkish. Methods: Methodological study with a non-probabilistic sample of 209 hospital nurses, conducted between June and July 2024. The linguistic adaptation involved translation and back-translation with the participation of bilingual experts. Statistical analyses included exploratory and confirmatory factor analyses, item-total correlation tests, test-retest reliability, and internal consistency assessment using Cronbach’s alpha. Results: One item was removed due to cultural incompatibility, resulting in a 24-item Turkish version of the NWMAS. During the adaptation process, expert evaluations led to the removal of one item from the original scale, as it referenced nursing practices that are either not widely implemented or considered culturally incompatible with the structure of the Turkish healthcare system. Content Validity Index values ranged from 0.85 to 0.95. Exploratory factor analysis confirmed a five-factor structure explaining 55.65% of total variance. Confirmatory factor analysis supported this structure with acceptable fit indices (χ²/df = 1.89; RMSEA = 0.06; GFI = 0.86). Cronbach’s alpha for the overall scale was 0.87, with subscale alphas ranging from 0.52 to 0.82. Test-retest reliability coefficients ranged from 0.95 to 0.98, indicating high stability over time. Conclusions: The Turkish version of the NWMAS demonstrated adequate validity and reliability and can be used to evaluate nurses’ work methods in Turkish hospital settings. The study highlights the importance of cultural adaptation in scale development to ensure conceptual relevance in local healthcare systems. Full article