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Impact of asynchronous online cancer education for nurses on practice outcomes: A systematic review using the kirkpatrick evaluation model
Full text linkBackgroundContinuing education is critical for oncology nurses to enable the delivery of safe, efficient, specialist care based on current evidence-based guidelines. Accessing training opportunities can be challenging in the face of work and staffing pressures. Asynchronous online education can circumvent this barrier but despite growth in popularity, evidence of its effectiveness on practice outcomes is weak.AimTo evaluate and synthesise evidence for the impact of asynchronous online cancer education for nurses on practice outcomes.MethodsA systematic approach was used to identify primary studies in the following databases: CINAHL, MEDLINE, APA, PsychInfo, ERIC, EMBASE, PubMed and Web of Science. Critical appraisal was guided by the Medical Education Research Quality Instrument (MERSQI) and Effective Public Health Practice Project (EPHPP) tools for quantitative studies, and the Critical Appraisal Skills Programme (CASP) checklist for qualitative studies. The Kirkpatrick Four Level Model of training evaluation and a modified effect direct plot were used to facilitate synthesis of the evidence.FindingsThirty studies of varying research design were included in the review. Vast heterogeneity was apparent in research designs, participants, intervention design and duration, and outcome measures. Due to this heterogeneity, it was difficult to establish that one design of education was more effective than another. Patient outcomes were not measured in any of the thirty studies reviewed. Behavioural change was objectively measured in two (7 %) studies, evaluating screening rates and pain assessments, and self-reported in eleven (37 %) studies regarding changes such as adapting to patients' language needs, cytotoxic drug handling, and discussing reproductive health. At least one educational outcome (nurses' knowledge, skills, attitudes, beliefs, confidence or self-efficacy) was measured in all thirty papers, however, improvements in these did not necessarily translate to evidence of changing practice outcomes. The overall quality of evidence was weak, particularly with respect to research design and validity of outcome measures.ConclusionThe need for more rigorous research in this field continues as there were limited evidence available to evaluate the direct impact of online cancer education on practice outcomes. Practice outcome evaluation could be enhanced by using measurements that are less subject to bias
Imagining and experiencing hospitalities and interculturalities in a mobile world
No full textAbstract not available
Co-Designing a Socially Assistive Robot Intervention for Diabetes Self-Management: Perspectives of Adults Living with Diabetes
Full text linkOver 537 million adults live with diabetes, yet many lack sufficient support for effective self-management, resulting in suboptimal glycaemic levels and rising healthcare costs. Socially assistive robots (SARs) may fill this gap through offering real-time, personalised support. This study explored adults’ views on the design, usability, and acceptability of a SAR-based intervention. A three-phase exploratory co-design approach, guided by the Medical Research Council framework, involved 16 adults with type 1 and 2 diabetes in Scotland. Phase 1 involved forming a working group and conducting a scoping review. Phase 2 included two online focus groups to identify challenges, technology use, and desired SAR features; and two in-person workshops at the UK National Robotarium to assess SAR’s usability, acceptability, and integration into daily life. Phase 3 involved thematic analysis, translating user requirements, and planning future feasibility. Participants identified barriers including dietary adherence, insulin adjustments, and limited access to clinicians. Technologies like glucose monitors and insulin pumps were seen as burdensome, highlighting the need for personalised, on-demand support. In conceptualising a SAR, participants recommended interactive coaching features, such as real-time medication guidance, meal planning, emergency alerts, and data-sharing. Priorities included a user-friendly, inclusive design, strong data privacy, and integration with existing devices and care pathways. SARs offer a feasible approach to personalised self-management, with user-driven features like tailored coaching, accessible design, and healthcare connectivity improving glycaemic levels and well-being. These findings inform prototyping and evaluation of a new SAR intervention for diabetes management
Predefined‐Time Intelligent Switching Tracking Algorithm for Multiagent Ship Maneuvering Systems With Intermittent Performance Constraints
No full textThis paper investigates the adaptive predefined-time tracking issue for a class of multi-ship maneuvering systems subject to input delays. Firstly, an intelligent distributed switching tracking algorithm is designed such that each ship unit can respond to local information independently while maintaining overall multi-agent systems coordination. Based on the proposed algorithm, a neural network predefined-time controller integrates a finite-time differentiator with Pade approximation to deal with the issues caused by “complexity explosion” and input delay. In addition, the considered intermittent output constraints can be adjusted flexibly and effectively eliminated by barrier functions and shift functions. Furthermore, the proposed controller can ensure that all signals in the closed-loop system can remain bounded and the output of the system achieves the desired tracking signal within a predefined time. Finally, to verify the effectiveness, the proposed strategy is applied to multi-ship manoeuvring systems with external disturbances and input time delays
Frequency-aware selective state-space modeling for audio-visual speech enhancement
No full textAudiovisual speech enhancement improves speech intelligibility by using auditory and visual modalities, especially in noisy environments where audio-only speech enhancement performs weakly. Visual cues provide complementary information to disambiguate acoustically degraded speech signals. Transformers offer powerful long-range dependency modeling at the cost of quadratic complexity, posing challenges for real-time applications. To address the limitation, this study proposes a novel architecture that integrates a Frequency-Aware Mamba (FAM) module coupled with a Multimodal Query Transformer (MQT) as a bottleneck. FAM extends the Mamba selective state-space model (SSM) by incorporating frequency awareness, allowing it to efficiently model complex spectro-temporal dynamics with linear complexity. MQT uses a query-based attention process for selective focus on salient cross-modal features, ensuring multimodal alignment. To improve multimodal feature integration, this study proposes a cross-attentive gated fusion module that replaces naive concatenation. This fusion learns cross-modal relevance and dynamically adjusts the contribution of audio-visual cues using a gating mechanism, resulting in more discriminative and noise-robust latent features. The proposed model provides an effective balance between quality and computational efficiency. Experimental results on the benchmark datasets demonstrate significant performance gains while maintaining a low computational cost
Perceptions and experiences of the menstrual cycle amongst elite adult and adolescent football players
Full text linkThe purpose of this study was to investigate players’ experiences and perceptions of the menstrual cycle (MC) and the perceived impact on performance. Female elite adult (n = 31, age 24.6 ± 5.1 years) and adolescent (n = 65, age 15.0 ± 1.1 years) players completed an online questionnaire consisting of quantitative and qualitative questions. MC symptoms were experienced by 90.1% naturally menstruating participants (86.9% adolescents and 93.6% adults (x2 = 1.53, df = 2, p = 0.47, n = 92)), and 78.3% adolescents perceived their MC impacts performance, compared to 96.4% adults (x2 = 4.54, df = 1, p = 0.033, n = 74). Physical symptoms, psychological symptoms and energy levels were cited as key reasons for the MC negatively impacting performance. Challenges in communicating MC experiences were reported by 44.92% (n = 23) adolescents compared to 20.0% (n = 6) adults (x2 = 7.29, df = 2, p = 0.026, n = 82), with a perceived lack of knowledge, ability to relate and awkwardness cited as key reasons. Football players report wellbeing and performance impacts due to their MC, highlighting the need for individual understanding and support. Furthermore, understanding the experiences of adolescents enables the development of targeted support structures that equip them with tools to manage and communicate about their MC, and hopefully preventing issues as they become senior players
Using Frequency B-Splines for an accurate and faster calculation of adaptive transforms for electric machines diagnosis
No full textEarly detection of faults in electric motors is crucial to prevent unplanned downtime and expensive repairs. Transient analysis through time-frequency transforms reveals important information on the motor condition. Computational time of these transforms becomes a problem when dealing with thousands of motors in just one industry. Researchers focus on obtaining the best quality results, usually using Gabor functions as t-f atoms. This paper shows how Frequency B-Splines can reach the same quality with nearly 40% less computational time. To achieve this goal, the slope criterion is applied to select the optimal atoms parameters. A formula that relates the slope of the Heisenberg Box of a FBS with its parameters is deduced. The proper time interval where the FBS must be defined is also determined. Results are shown not only with lab tests, but also through a field case
Towards Scientific Machine Learning for Granular Material Simulations: Challenges and Opportunities
Full text linkMicro-scale mechanisms, such as inter-particle and particle-fluid interactions, govern the behaviour of granular systems. While particle-scale simulations provide detailed insights into these interactions, their computational cost is often prohibitive. At a recent Lorentz Center Workshop on “Machine Learning for Discrete Granular Media”, researchers explored how machine learning approaches can aid the development of constitutive laws and efficient data-driven surrogates for granular materials while also addressing uncertainty quantification. Attended by researchers from both the granular materials (GM) and machine learning (ML) communities, the workshop brought the ML community up to date with GM challenges. This position paper emerged from the workshop discussions. In this position paper, we define granular materials and identify seven key challenges that characterise their distinctive behaviour across various scales and regimes–ranging from gas-like to fluid-like and solid-like. Addressing these challenges is essential for developing robust and efficient models for the digital twinning of granular systems in various industrial applications. To showcase the potential of ML to the GM community, we present classical and emerging machine/deep learning techniques that have been, or could be, applied to granular materials. We reviewed sequence-based learning models for path-dependent constitutive behaviour, followed by encoder-decoder type models for representing high-dimensional data in reduced spaces. We then explore graph neural networks and recent advances in neural operator learning. The latter captures the emerging field evolution of interacting particles via efficient latent space representation. Lastly, we discuss model-order reduction and probabilistic learning techniques for high-dimensional parameterised systems, both of which are crucial for quantifying and incorporating uncertainties arising from physics-based and data-driven models. We present a typical workflow aimed at unifying data structures and modelling pipelines and guiding readers through the selection, training, and deployment of ML surrogates for granular material simulations. Finally, we illustrate the workflow’s practical use with two representative examples, focusing on granular materials in solid-like and fluid-like regimes
Visual Methods in/as Leisure Research: A Visualized Essay
No full textOur visualized essay introduces, positions, and responds to the articles contributing to this special issue, Visual Methods in/as Leisure Research. We present a series of our personally selected images alongside a collaboratively produced narrative. By adopting this innovative approach, our aim is to create a thematic visual and written chronicle of four thematic clusters that interlace with the methodological and conceptual threads of the eleven articles in this special issue. The thematic clusters are: seeing leisure otherwise, making visible, digital visualities, and imagining futures. The individual contributions of the articles in this special issue invite us to expand epistemic horizons. By reflecting on and responding to these, in this visualized essay, we conclude that visual inquiry is not merely an illustrative supplement to leisure research. Rather, it is a methodological and epistemological stance which invites new ways of seeing, knowing, and being, with leisure. Furthermore, visual methods in/as leisure are central to rethinking what leisure research can do and who it can include
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