Halmstad University

The worldwide acceleration of digital transformation in healthcare underscores the need for professionals to continuously adapt and sustain robust digital health competence, shaped not only by individual characteristics and institutional environments but also by broader social, cultural, and geopolitical factors. This study aimed to identify distinct clusters of digital health competence among healthcare professionals across 19 diverse countries and regions, and to examine the factors influencing the development and distribution of these competence clusters. A cross-sectional international survey study was conducted between 2023 and 2024, using a validated survey instrument measuring digital health competence and its influencing factors. Data were collected from healthcare professionals in 19 countries and regions (n=6440; n=5945 used for this study), following a harmonised protocol with shared demographic templates and instruments. K-means cluster analysis was employed to derive digital competence profiles, with comparative analyses conducted to investigate associations between the identified clusters and individual characteristics (e.g., age, education, professional experience). Five distinct clusters of digital health competence were identified: (1) Beginners, (2) Developing Professionals, (3) Emerging Users, (4) Proficient Practitioners, and (5) Pioneers. Higher competence clusters (4 and 5) were associated with younger age, higher education, hospital-based work, and stronger perceived support from management, organisational structures, and colleagues. In contrast, lower-performing clusters reported limited digital engagement and minimal support. Perceived leadership influence, particularly managerial commitment to digital change, was a key differentiator across clusters. The findings demonstrate substantial variation in digital health competence across healthcare professionals internationally. Cluster-specific strategies, such as targeted upskilling, peer mentoring, and leadership engagement, are needed to address competence gaps. The results provide a foundation for policy development and workforce training frameworks aimed at strengthening digital readiness in global healthcare systems. Future research should explore longitudinal competence development and evaluate targeted interventions.

This study examines how a multidimensional framework can support instructional analysis and competence development in preschool teacher education. Conducted as a two-year action research project, it involved eight practising preschool teachers and 83 student teachers who analysed instructional sequences filmed during their teaching practicum periods. These analyses, carried out in seminars led by the practising preschool teachers, used the three dimensions of the framework – ‘what’ (learning object), ‘how’ (instructional methods), and ‘when’ (relational timing and pedagogical tact) – as an analytical lens to explore how participants understood and applied these dimensions and what opportunities and challenges emerged. The findings show that the framework helped participants articulate intentional learning objectives, distinguish activities from learning objects, and align instructional methods to specific aims. It also made visible aspects of professional judgement that are often tacit, particularly the situational responsiveness captured by the ‘when’ dimension. Although socio-emotional considerations were frequently highlighted, their cognitive significance was less explicitly recognised, indicating a need for frameworks that connect affective and cognitive dimensions of teaching. The study demonstrates that the structured analytical framework, when combined with video-based reflection, can strengthen learning during practicum and support continuous professional development. The framework balances intentional instruction with responsive, child-centred pedagogy and provides a shared language for discussing the interrelation of the ‘what’, ‘how’, and ‘when’ dimensions in preschool teaching.

Abstract Artificial intelligence (AI) systems for decision-support are being introduced across multiple domains. One precondition for successful implementation is that those affected by the decisions trust the AI system. In the literature, many drivers of trust in AI have been proposed. In this study, we investigate the relative importance of six potential drivers of trust across three different decision-making domains, i.e., finance, legal, and healthcare in the Danish population. Based on an initial literature review and focus groups we designed a discrete choice study using conjoint analysis. Respondents were presented with a decision involving an AI system and asked to choose which of three possible AI systems they would trust the most, each system being characterized by a combination of levels of six attributes. All participants were presented with six choice situations. The decisions presented in the three domains were chosen to have approximately equal importance based on the focus group discussions. The three decisions were decision-making about a loan for a house purchase, decision-making about guilt in a case of assault, and diagnosis of skin cancer in primary care. Three samples of approximately 300 respondents each, representative of the Danish adult population were recruited through the panel provider Wilke. The main findings are that the most important drivers of trust differ between domains, with professional responsibility for the final decision being most important in finance and legal contexts, and precision being most important in the healthcare domain. Across all three domains, the absence of a particular trust-making feature has a strong negative effect, stronger than the presence of the same feature.

ABSTRACTPacing is a critical determinant of performance in Olympic endurance winter sports such as cross‐country skiing, biathlon, and speed skating. Although the physiological (“engine”) and psychological (“operator”) determinants of pacing have frequently been examined in isolation, growing evidence highlights the need for an integrated psychophysiological perspective. This structured narrative review first outlines the defining characteristics of Olympic winter sports and, within this context, synthesizes current knowledge on the psychophysiological mechanisms governing pacing to inform optimal preparation strategies. We describe how the physiological engine, encompassing energy systems, fatigue, and afferent feedback, and the psychological operator, involving self‐regulation, expectations, and cognitive‐affective processes, interact to shape pacing behavior. Moreover, the distinctive demands of winter endurance events necessitate consideration of environmental factors such as head‐to‐head racing formats, variable weather conditions, and undulating terrain. The affordance competition hypothesis is proposed as a unifying framework for understanding pacing as a continuous decision‐making process driven by dynamic mind‐muscle‐environment interactions. Finally, practical recommendations for athletes and coaches preparing for the Milano–Cortina 2026 Winter Olympic Games are presented, advocating a phased training approach that integrates physiological and psychological development within ecologically valid environments. Future research should prioritize real‐world competition settings, neurocognitive mechanisms, and underrepresented populations, including women and para‐athletes. We conclude that Olympic success in modern endurance winter sports depends on mastering an integrated psychophysiological control system, where performance is determined by both physical capacity and its effective regulation under varying environmental conditions.

This critique responds to Eldh et al.'s (Implement Sci Commun 6:113, 2025) commentary on Nilsen et al.'s proposal to distinguish between implementation efficacy and effectiveness along an ideal-to-real-world continuum. While acknowledging the constructive intent of Eldh et al.'s reflections, we clarify that our framework was never intended as a simplistic, one-dimensional model but as a pragmatic heuristic to enhance design transparency. Eldh et al.'s proposed two-axis alternative is conceptually overlapping, as both axes reflect contextual variation rather than independent constructs. Our adaptation of the PRECIS framework - long validated in clinical and health services research - already incorporates multidimensional nuance through distinct domains. We emphasize that the "ideal" end of the continuum denotes highly supported conditions, not normative perfection. Moreover, the proposed "Implementation PRECIS" tool is intended to stimulate integration of contextual transparency and economic evaluation within implementation research. While we concur with Eldh et al.'s emphasis on facilitation, co-production, and contextual complexity, their critique ultimately reinforces our core premise: that explicitly positioning studies along an efficacy-effectiveness spectrum strengthens interpretability, transparency, and real-world relevance in implementation science.

PlayerLoad™ is the most widely used accelerometer-derived metric for quantifying external demands in sport. Its normalized variant, PlayerLoad·min−1, is also commonly used as a marker of exercise intensity. However, recent literature has raised concerns regarding its scientific foundation, including inconsistent definitions, arbitrary units, opaque filtering methods, questionable theoretical underpinnings, and imprecise mechanical terminology. The construct validity of PlayerLoad™ remains unverified, and emerging evidence suggests weak dose–response relationships with performance outcomes. Although widely adopted in practice, these concerns warrant critical scientific scrutiny. This review critically evaluates the validity and reliability of the PlayerLoad™ metric, highlighting the need for greater transparency and theoretical rigor in wearable athlete monitoring. Furthermore, we present alternative accelerometer-derived metrics, developed from clearer biomechanical and physiological principles, which may offer more robust and interpretable measures for researchers and practitioners.

With large-scale language models demonstrating superior capabilities in a wide range of downstream natural language processing tasks, the future trajectory of research in the field of text categorization faces increasing uncertainty. In this evolving paradigm of open-ended language modeling, where task delimitations are increasingly blurred, a pressing question arises: to what extent has text classification advanced under the full potential of large language model (LLM)? To address this pivotal inquiry, we introduce recurrent generative pre-trained transformer (RGPT), an adaptive boosting framework meticulously designed to craft a dedicated LLM for text classification. RGPT constructs a sequence of base learners by dynamically modulating the training data distribution and iteratively fine-tuning LLMs. These base learners are then progressively integrated, leveraging historical prediction trajectories to form a highly specialized text classification model. Extensive empirical evaluations demonstrate that RGPT surpasses eight state-of-the-art pretrained language models and seven cutting-edge LLMs across four benchmark datasets, achieving an average performance gain of 2.90%.

ObjectivesTo develop a sustainable and accessible educational programme on biopsychosocial female-specific topics, for young female athletes in organised sport, their parents and coaches for future testing and evaluation in a full-scale intervention study.MethodsA digital educational programme, along with its methodology, was developed in four steps: First, content validity of female-specific topics was identified through (a) literature reviews by an interdisciplinary research team and (b) an online survey of 1323 female athletes (aged 16–24 years) assessing perceived knowledge and knowledge needs regarding biopsychosocial topics. Second, film scripts based on identified topics were written, internally reviewed and evaluated by a pilot group of six 13–16-year-old female athletes. Third, online surveys assessing main and secondary outcomes were developed and tested in the pilot group. Finally, digital format and production were developed collaboratively with a film scholar and film technicians.ResultsThe intervention development yielded 12 videos of 5–7 min, featuring sports science researchers and female elite athletes sharing their perspectives on biopsychosocial topics specific to women. The protocol development produced a quasi-experimental, modified staircase cluster trial design, including bi-weekly assessment of main (ie, well-being and sporting experiences) and secondary outcomes (eg, perceived support) at baseline, pre-, post-intervention and post-post follow-up.ConclusionThis study outlines the development of a digital educational intervention for young female athletes, their parents and coaches, emphasising that interdisciplinary collaboration and user engagement are key to identifying intervention components and relevant outcomes.