Search Results (2,081)

The rapid advancement of laboratory automation, robotics, and digital technologies has significantly transformed laboratory medicine worldwide, improving efficiency, diagnostic accuracy, and quality management. However, the adoption of these technologies in developing countries such as Indonesia remains uneven and is influenced by infrastructural, financial, regulatory, and workforce-related challenges. This structured narrative review aimed to critically examine the current landscape of laboratory automation and robotics in Indonesia, with particular emphasis on implementation challenges, workforce transformation among medical laboratory scientists (Ahli Teknologi Laboratorium Medik, ATLM), and pathways toward equitable integration. Studies published between 2015 and 2025 were identified through PubMed, Scopus, and Google Scholar, complemented by Indonesian regulatory documents, professional guidelines, and relevant grey literature. The review was informed by PRISMA principles and synthesized narratively to explore technological developments, operational impacts, policy contexts, and implementation barriers relevant to Indonesian laboratory systems. The findings indicate that automation and robotics offer substantial benefits, including improved turnaround time, enhanced quality assurance, reduced laboratory errors, and greater operational efficiency. Nevertheless, significant barriers persist, particularly disparities in digital infrastructure, financial constraints, limited workforce readiness, and the absence of comprehensive implementation frameworks. The review further highlights that automation is reshaping rather than replacing the role of ATLM, shifting professional responsibilities toward digital competency, automation oversight, data interpretation, and quality management. Achieving sustainable laboratory automation in Indonesia therefore requires an equity-centered and systems-oriented approach involving regulatory strengthening, workforce development, infrastructure investment, and multi-stakeholder collaboration. With strategic planning and policy alignment, laboratory automation and robotics hold considerable potential to modernize laboratory services and support Indonesia’s broader healthcare transformation agenda. Full article

Data has become a strategic production factor, but the institutional logic underlying data’s tradability, priceability, and governability remains insufficiently theorized. In response, this study develops a coevolutionary framework that connects conventional factor market theory with digital political economy, platform theory, and comparative institutional analysis. This study adopts a conceptual–analytical research design, integrating three research methods: theory synthesis, comparative institutional analysis, and policy-process interpretation. Through theoretical synthesis, institutional comparison, and policy-process interpretation, it analyzes the conditions under which data circulation becomes feasible, lawful, and economically sustainable. In addition, by combining transaction data, exchange listings, property rights registrations, network indicators, and regional policy variations, it formulates testable propositions and an empirical agenda. The study finds that data factor markets do not emerge automatically with digitalization; their formation requires three mutually reinforcing conditions: technologically reducing search, verification, privacy protection, and contract enforcement costs; institutionally realizing a modular definition of rights and establishing compliance boundaries; and market demand from firms, public agencies, and research organizations generating use-case-specific value. Meanwhile, this study revises the three-stage model of market evolution as a contingent and testable pathway—from administrative pilot allocation, through hybrid state–market professionalization, to ecosystem-based cross-domain circulation. It also clarifies a closed-loop dynamic mechanism consisting of external shocks, internal strategic feedback, and adaptive governance, which jointly shapes market boundaries, pricing rules, and competition patterns. Full article

Ornamental aquarium keeping collectively involves millions of freshwater, marine, and reef systems in which fish, corals, invertebrates, biofilters, microbial communities, and human husbandry practices are closely interconnected. In these domestic aquatic animal systems, preventive and curative treatments may include antimicrobials, antiparasitics, antiseptics, oxidizing agents, copper-based products, dips, and commercial formulations targeting microbial proliferations or visible system deterioration. Many interventions occur without veterinary diagnosis, microbiological confirmation, standardized dosing, active-ingredient transparency, or post-treatment monitoring. This raises concerns for aquatic animal health and welfare, as whole-system treatments may affect not only the intended pathogen or pest but also non-target organisms, biofilter communities, animal-associated microbiota, and water quality stability. Digital communities and online platforms can rapidly circulate empirical treatment protocols, although they may also provide opportunities for stewardship education and improved husbandry guidance. Current evidence does not support interpreting ornamental aquaria as major independent drivers of antimicrobial resistance. The more defensible concern is stewardship: biologically active compounds may be used repeatedly and empirically in animal systems without diagnosis, professional guidance, or systematic monitoring. This Perspective argues that ornamental aquaria should be recognized as an overlooked interface between aquatic animal health, welfare, antimicrobial stewardship, and One Health. It proposes a research and communication agenda focused on treatment transparency, diagnosis, prevention, biofilter protection, and responsible care practices. Full article

The accumulation of training and competition loads represents a critical determinant of musculoskeletal injury risk in professional team sports, yet contemporary monitoring systems remain limited by their reliance on single-domain temporal analysis that overlooks the multi-scale rhythmic patterns inherent in athletic workload signals. This study introduces FDTM (frequency-aware dual-stream temporal model), a deep learning framework that jointly encodes time-domain dependencies and frequency-domain spectral signatures from digital athlete monitoring streams to predict individual injury risk over a forward-looking seven-game horizon. The framework integrates a stacked bidirectional long short-term memory branch augmented with temporal self-attention pooling, a spectral encoding branch employing discrete Fourier transform decomposition across high-frequency (weekly), mid-frequency (bi-weekly), and low-frequency (seasonal) bands, and a cross-modal gated attention fusion module that adaptively balances temporal and spectral representations conditioned on player context. We evaluate FDTM on three heterogeneous public sports datasets spanning basketball (NBA game-log corpus 2013–2023), Australian rules football (AFL Player Workload Dataset), and soccer (SoccerMon open monitoring corpus), comprising 612 athletes and 247,830 player-game observations across ten competitive seasons. FDTM achieves AUC-ROC values of 0.858, 0.833, and 0.821 on the three datasets respectively, outperforming the strongest deep-learning baseline (FEDformer) by 2.0 to 3.3 percentage points and the strongest non-spectral baseline (TCN) by 3.2 to 4.5 percentage points while maintaining a Brier score below 0.04. Ablation studies confirm that the spectral branch contributes 5.1 percent to overall discriminative performance. SHAP attribution analyses identify high-frequency weekly components as the dominant injury-relevant signal, followed by low-frequency seasonal trends and the cumulative acute-to-chronic workload temporal feature, with gating-weight visualizations revealing dynamic modality contributions consistent with established sports science theory. Direct spectral analysis of the raw workload signal confirms that injury-preceding windows exhibit significantly elevated weekly-band power across all three datasets (Mann–Whitney U test, p < 1 × 10⁻⁷), and the architectural advantage is shown to be robust across 30 independent training seeds. These findings suggest that frequency-aware modeling may serve as a transferable methodology for sports engineering applications in injury prevention, return-to-play planning, and individualized rehabilitation, pending further external validation in female athletes and additional team sports. Full article

Generative artificial intelligence (GenAI) is rapidly reshaping higher education, prompting institutions to reconsider how students demonstrate capability in an environment where text can be generated, refined, and summarised with ease. Previous research shows that students use GenAI to improve efficiency, reduce workload, and support comprehension, yet continue to seek clearer guidance on acceptable use. This gap between widespread adoption and limited pedagogical direction underscores the need for structured approaches that help students understand how, when and why GenAI can be used. Within postgraduate health leadership education, cultivating digital literacy, critical reasoning, and ethical judgement are important, as future leaders must model responsible technology use within their organisations. This mixed-methods, cross-sectional, online survey pilot study examined whether embedding ethical GenAI use within assessment tasks reduced the likelihood of students engaging in additional unethical GenAI practices. The study also explored students’ motivations for using GenAI, perceived barriers to ethical engagement, acceptance or rejection of GenAI outputs, and the transferability of GenAI skills to professional practice. Thirty-nine responses from previous students provide early insight into how structured, ethical integration of GenAI within assessment design may influence student behaviour and support the development of responsible, critically informed GenAI use in postgraduate health leadership programs. Full article

Public Relations (PR) is the strategic role of communication between an organization or public actor and the publics on whose recognition, trust and consent its operation depends. The field encompasses media relations, internal and employee communication, public affairs and government relations, crisis and reputation management, community relations and corporate social responsibility, investor relations, marketing communications, political communication, digital and platform engagement, and the communication of activist and non-governmental organizations. PR scholarship draws on sociology, political philosophy, organizational studies, rhetoric and media studies to examine how organizations construct, sustain and contest their legitimacy in the public sphere. The dominant theoretical framework of the late twentieth century, the Excellence Theory developed by James Grunig and colleagues, defined PR as the symmetrical management of relationships with strategic publics; the field has since broadened to include relational, dialogic, rhetorical, situational, contingent and critical approaches, alongside frameworks grounded in stakeholder theory, institutional analysis and emerging work on public legitimacy. Contemporary PR operates within a hybrid media environment shaped by digital platforms, algorithmic visibility, generative artificial intelligence and the structural erosion of institutional trust. It is simultaneously a professional industry of significant global economic scale and a contested civic function whose democratic role, ethical foundations and disciplinary boundaries remain the subject of active scholarly debate. Full article

Background: As a crucial transitional period from campus to society, providing comprehensive undergraduate health psychological care is essential for addressing Social Anxiety Disorder (SAD). Current global healthcare research is actively exploring innovative digital interventions, with a specific focus on leveraging Augmented Reality (AR) as a transformative auxiliary tool in clinical settings. Methods: This study investigates the factors influencing the acceptance of AR-assisted Cognitive Behavioral Therapy (CBT) within student healthcare frameworks by developing a research model based on the Technology Acceptance Model (TAM). The framework incorporates key clinical and behavioral constructs: self-efficacy (SE), facilitating conditions (FC), and social influence (SI). Results: SE, FC, and SI significantly and positively impact the willingness to adopt AR technology for mental health purposes. Based on these findings, practical recommendations are provided for healthcare technology developers, therapists, and university psychological care providers to enhance the integration of AR-assisted CBT. Conclusions: Strengthening these digital pathways is vital for improving healthcare outcomes and enabling students to navigate future social and professional environments effectively. Because the sample consisted solely of Chinese undergraduate students, the findings should be interpreted within this specific cultural and educational context and require further validation in cross-cultural and multi-regional samples. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly embedded within diagnostic imaging workflows, reshaping clinical decision-making, health system governance, and regulatory oversight. While technical advances in radiological AI have accelerated, governance mechanisms have struggled to keep pace with issues of bias, transparency, accountability, and lifecycle oversight. This study examines ethical, regulatory, and implementation challenges in AI-enabled diagnostic imaging, building on prior reviews that have often emphasised technical performance by integrating ethical risk domains with governance responses across the AI lifecycle. Methods: This study presents a PRISMA-ScR-informed systematic survey of 156 sources, including peer-reviewed publications, regulatory documents, policy reports, and professional guidance materials (2018–2025), synthesised through thematic analysis and lifecycle mapping spanning data acquisition, model development, deployment, monitoring, and continuous learning. Results: Drawing on both thematic insights derived from the reviewed literature and established ethical and regulatory frameworks, we propose a literature-derived conceptual ethical-governance framework organised around five pillars: equity and bias mitigation, explainability and transparency, accountability and oversight, privacy-preserving infrastructure, and adaptive regulatory alignment. Although illustrated through the Australian healthcare context, the framework is designed to be transferable to federated and multi-jurisdictional health systems. This review further identifies trust quantification as an underdeveloped but essential dimension of clinical AI governance, emphasising the need to integrate measurable indicators such as calibration, clinician–AI concordance, and patient acceptance into lifecycle-based evaluation. Conclusions: By bridging technical, ethical, and policy perspectives, this review proposes a structured conceptual governance framework to support safe, equitable, and trustworthy AI integration in digital health systems. Full article

Background and Aim: While the proliferation of digital health technologies and wearable devices provides nursing professionals with constant access to biometric data, the pathological reliance on these metrics represents an emerging, yet empirically unexamined, digital anxiety framework. This study aims to theoretically define and systematically analyze this theorized phenomenon—termed “Technohypochondria”—within the context of nursing management and clinical practice. Methods: Utilizing Walker and Avant’s eight-stage concept analysis method, a systematic literature search was conducted across PubMed, CINAHL, Scopus, and Web of Science databases. Following strict inclusion and exclusion criteria, a total of 1240 data sources spanning nursing, management, psychology, and informatics literature were analyzed. Results: Three defining attributes of Technohypochondria emerged inductively from the literature: (1) Biometric data obsession, (2) Digital misinterpretation and catastrophizing, and (3) Need for algorithmic feedback. Unlike the general informational search patterns of cyberchondria, these attributes specifically capture a continuous, device-driven feedback loop. Ownership of wearable technology and inadequate digital health literacy were identified as primary antecedents. The analysis revealed significant managerial consequences, including loss of clinical focus, increased risk of medical errors, and weakened professional autonomy. Conclusions: Technohypochondria operationalizes a specific anxiety framework driven by constant biometric monitoring, conceptually diverging from cyberchondria’s focus on online health-information seeking. For nursing managers, addressing the psychological relationship between staff and technology is a strategic necessity for patient safety and workforce productivity. A primary limitation of this study is its theoretical nature; however, this study provides the essential conceptual foundation awaiting future empirical validation and scale development. Full article

Objective: To map the scientific evidence and identify knowledge gaps regarding the health of Black and Sexual and Gender Minority populations within the global context of health education. Introduction: Health education curricula should explicitly recognize, define, and address the specific needs and health disparities affecting Black and Sexual and Gender Minority populations to ensure that healthcare provision is comprehensive and inclusive in diverse settings. Eligibility criteria: Studies related to professional health training at undergraduate and graduate levels, as well as other educational modalities addressing healthcare provision for Black and Sexual and Gender Minority populations, were included. Methods: This scoping review was conducted following the JBI methodology. Studies were retrieved from Scopus, Web of Science, PubMed, Embase, MEDLINE, Virtual Health Library, CINAHL, ERIC, Cochrane Library, Brazilian Digital Library of Theses and Dissertations, ProQuest Dissertations & Theses Global, EBSCO databases, and the Networked Digital Library of Theses and Dissertations, without language or time restrictions. Two independent reviewers screened the studies and extracted data using a standardized form developed for this review. Concepts, definitions, structures, results, and applications of professional health education for the care of Black and Sexual and Gender Minority populations were systematically synthesized. The results were organized and presented in tabular and graphical formats, accompanied by a narrative summary. Results: A total of 104 studies were included. The evidence was predominantly concentrated in North America, particularly in the United States, with limited representation from other regions. Most studies were published after 2020, indicating a recent expansion of research interest. The methodological profile was characterized by a predominance of quantitative and descriptive designs, alongside qualitative and mixed-methods approaches. Thematic analysis revealed a concentration of studies addressing gender-affirming care, workforce diversity, social determinants of health, and discrimination, while intersectional approaches and long-term educational outcomes remained less explored. Conclusions: The available evidence indicates that health education has increasingly incorporated themes related to equity and diversity; however, the integration of structured and mandatory curricular approaches addressing the intersectional health needs of Black and Sexual and Gender Minority populations remains limited. The findings highlight the need for broader geographic representation, stronger methodological designs, and the development of comprehensive educational strategies capable of addressing structural inequalities within health training contexts. Full article

The transformation of the world of work, driven by economic and social change, technological advances, digitalization, and new working environments, has highlighted the importance of soft skills in vocational education and training (VET). In this context, workplace internships constitute a key setting for their development and assessment. This systematic review analyzes the recent literature on transferable skills in VET and their relationship with work-based learning environments. Following PRISMA guidelines, searches were conducted in the Web of Science and Scopus databases. After applying inclusion and exclusion criteria, 26 studies were selected from 1114 records. Findings reveal international consensus regarding the importance of communication, teamwork, problem-solving, and socio-emotional skills for labor market integration. However, a gap persists between workplace demands and training practices. Although active methodologies and integrated learning models show promising results, important limitations remain regarding competency assessment, school-company coordination, and teacher preparation. The review also identifies research gaps, particularly the scarcity of longitudinal studies and validated instruments for assessing transferable skills in authentic VET contexts. Finally, the findings highlight the need for integrated school-company collaboration models that support more consistent assessment and professional development among students. Full article

The study synthesizes scientific evidence on the reconfiguration of teacher autonomy and professional judgment in the face of pedagogical automation within post-digital professionalism. Following the PRISMA guidelines and using searches of Scopus and Web of Science using eligibility criteria, the study analyzes evidence from 27 peer-reviewed documents to explore how algorithmic mediation influences decision-making in post-digital environments. The results indicate that while automation improves administrative efficiency, it frequently induces pedagogical deskilling and ethical dilemmas related to algorithmic vulnerability and invisible repair work. Key findings identify a shift toward a new professional profile characterized by socio-technical stewardship, where critical AI literacy and intersubjective attunement serve as defensive competencies against EdTech standardization. Based on the synthesized corpus, it is concluded that the analyzed literature highlights a trend where safeguarding teacher autonomy requires a transition from technical competence to ethical reflexivity, suggesting that automated systems should complement, rather than substitute for human pedagogical integrity. The systematic review protocol was registered on the OSF platform. Full article

Healthcare systems are complex adaptive environments in which clinical work, digital technologies, and organizational routines interact continuously, often challenging the integration of artificial intelligence (AI) into everyday practice. Although explainable AI (xAI) has been proposed to address concerns related to algorithmic opacity and professional trust, explainability is still frequently approached. Grounded in General Systems Theory, sociotechnical systems theory, and complexity science, this study conceptualizes explainability as an emergent system-level property of healthcare systems. Using Design Science Research as a systems-oriented inquiry methodology, a human-centered conceptual framework for AI-supported clinical decision-making was developed through iterative cycles of problem framing, design, demonstration, and evaluation. The framework was explored in rehabilitation nursing, a domain characterized by multidimensional patient data, longitudinal decision processes, and close professional–patient interaction. Iterative engagement with Rehabilitation Nursing Specialists informed design principles related to user participation, contextualized explanations, and workflow alignment. An exploratory evaluation with 144 Specialists assessed perceived usefulness, comprehensibility of explanations, and acceptance of AI-supported recommendations in realistic scenarios. The findings indicate that explainability is experienced not as a property of the algorithm alone, but as an outcome emerging from interactions between AI behavior, human interpretation, and organizational context. The framework shows potential to meaningfully support clinical decision-making in Rehabilitation Nursing by providing contextually aligned, human-centered explanatory outputs. Full article

Background: Healthcare institutions increasingly face sustainability challenges associated with environmental governance, operational efficiency, digital transformation, and social responsibility in the post-pandemic era. However, limited studies have comprehensively examined the organizational factors influencing ESG implementation and healthcare social sustainability practices among hospitals. Objective: This study aimed to investigate the organizational determinants of ESG implementation and healthcare social sustainability practices among Taiwanese hospitals and to explore how healthcare professionals and hospital administrators perceive sustainability implementation within post-pandemic healthcare environments. Methods: A convergent mixed methods design integrating quantitative surveys and qualitative interviews was employed. Exploratory factor analysis (EFA), correlation analysis, and multiple regression analysis were performed, and qualitative data were analyzed using thematic analysis. Results: A total of 135 valid questionnaires were analyzed, and qualitative data were obtained from three semi-structured interviews. ESG sustainability support demonstrated the strongest positive influence on healthcare social sustainability practices (β = 0.481, p < 0.001), followed by operational sustainability (β = 0.276, p < 0.01) and sustainability management capability (β = 0.214, p < 0.05). Organizational resource pressure did not significantly influence healthcare social sustainability practices. The qualitative findings converged with and expanded upon the quantitative results by highlighting the importance of leadership support, digital healthcare transformation, operational coordination, and community health promotion in facilitating ESG implementation and long-term healthcare sustainability. Conclusions: The integrated findings suggest that healthcare ESG implementation increasingly functions as a comprehensive sustainability governance strategy involving operational sustainability, digital healthcare transformation, healthcare accessibility, and social responsibility practices within post-pandemic healthcare environments. Strengthening sustainability support systems, governance capability, and operational resilience may facilitate long-term healthcare social sustainability implementation. Full article

Robo-advisors have expanded access to automated investment services, but many platforms continue to rely on relatively static onboarding procedures and limited forms of user interaction. This study examines how participants with investment experience respond to two next-generation robo-advisory design features: financial digital twins, understood as dynamic investor profiles that integrate goals, risk tolerance, cash-flow patterns, and anticipated life events, and conversational artificial intelligence (AI), understood as an interactive interface for explaining recommendations. Using a scenario-based randomized 2 × 2 online experiment, 336 adult respondents with self-reported investment experience, recruited through professional and academic networks, were assigned to one of four robo-advisor scenarios that varied the personalization architecture, standard profile versus digital twin, and the interface style, plain dashboard versus conversational AI, while holding the portfolio recommendation constant. The results show that digital-twin personalization increases perceived personalization and privacy concern, indicating that more adaptive advisory architectures may be viewed as both more relevant and more data-intensive. Conversational AI increases the perceived interactive quality of the advisory experience, while selected willingness-related patterns, especially in the combined digital-twin and conversational-AI condition, are treated as exploratory because several secondary composites displayed limited internal consistency. The strongest confirmatory emphasis is therefore placed on perceived personalization and privacy concern, and the remaining findings are best interpreted as scenario-based investor responses rather than evidence of actual adoption behavior or confirmed psychological mechanisms. The study contributes to behavioral FinTech research by clarifying the personalization–privacy tension in AI-enabled robo-advisory services and by offering design implications for more transparent, interactive, and responsibly personalized digital wealth-management systems. Full article