Training Workload Research Articles

Preoperative patient teaching practices and associated factors among nurses working at hospitals in West Shoa Zone, Ethiopia, 2022: a cross-sectional study

IntroductionPreoperative teaching is fundamental nursing activity in which a nurse educates the patient about surgery and what to anticipate following the procedure. It is a process via which nurses give standard preoperative information to patients before surgery, and it enables the patients to understand their diagnosis and treatment, actively participate in their own care, overcome feelings of incapacity in relation to their condition, regain health, and maintain home care. However, there is a dearth of studies that determine the extent of preoperative teaching practice in Ethiopia in general and in the study area in particular.ObjectiveThis study aimed to assess preoperative patient teaching practices and the factors associated with these practices among nurses working in hospitals in the West Shoa, Oromia region, Ethiopia, in 2022.MethodsAn institution-based cross-sectional study was conducted among 267 nurses from 1 September to 30 September 2022, at hospitals in the West Shoa Zone. Two-stage simple random sampling technique was used to select study participants. Data were collected using a pretested and structured self-administered questionnaire. The quantitative data were checked and entered using Epi-data version 4.6 and exported to SPSS version 26.0 for analysis. Bivariable and multivariable logistic analyses were performed, and p-values of <0.05 at a 95% confidence interval were considered statistically significant.ResultA total of 253 nurses returned the entire questionnaire, yielding a response rate of 94.75%. The study enrolled 132 (52.2%) male, the highest percentage (231, 91.3%) of the participants were in the age group of 18–35, the majority of participants (152, 60.1%) were married, and 164 (64.8%) were protestant. Approximately 101 (39.9%) participants demonstrated good preoperative teaching practice. Lack of teaching material, lack of training workload, time constraints, insufficient staffing, language barrier, severity of patient cases, patient and family’s anxiety, and complexity of patients’ status were significantly associated with preoperative patient teaching practice.ConclusionThe proportion of preoperative patient teaching practices among nurses working at hospitals in the West Shoa Zone was low. Concerned bodies should emphasize ways to improve preoperative teaching practice.

THE ASSESSMENT OF KNOWLEDGE AND PRACTICES OF NURSES REGARDING WOUND CARE

Proper wound care is critical for patient recovery and infection prevention. Inadequate knowledge and inconsistent practices among nurses can lead to suboptimal patient outcomes, particularly in low- and middle-income countries like Pakistan. Objective: To assess the knowledge and practices of nurses regarding wound care in a tertiary care hospital in Lahore, Pakistan. Methods: A descriptive cross-sectional study was conducted among 133 nurses working in medical wards, surgical wards, and ICUs. Data were collected using a structured questionnaire assessing demographic characteristics, knowledge, and practices related to wound care. Descriptive and inferential statistics were analyzed using SPSS version 26. Results: Among the participants, 73.7% demonstrated good knowledge of wound care, while 26.3% had poor knowledge. However, only 54.1% exhibited good practices, with 45.9% demonstrating suboptimal adherence to wound care protocols. Factors such as insufficient training and high workloads were identified as potential barriers to effective practice. Conclusion: Although nurses possessed good theoretical knowledge of wound care, significant gaps in practice were observed. Targeted training, resource provision, and strict adherence to evidence-based guidelines are essential for improving wound care practices and reducing the incidence of SSIs in Pakistan.

A Data-Loader Tunable Knob to Shorten GPU Idleness for Distributed Deep Learning

Deep Neural Networks (DNNs) have been applied as an effective machine learning algorithm to tackle problems in different domains. However, the endeavor to train sophisticated DNN models can stretch from days into weeks, presenting substantial obstacles in the realm of research focused on large-scale DNN architectures. Distributed Deep Learning (DDL) contributes to accelerating DNN training by distributing training workloads across multiple computation accelerators, for example, graphics processing units (GPUs). Despite the considerable amount of research directed toward enhancing DDL training, the influence of data loading on GPU utilization and overall training efficacy remains relatively overlooked. It is non-trivial to optimize data-loading in DDL applications that need intensive central processing unit (CPU) and input/output (I/O) resources to process enormous training data. When multiple DDL applications are deployed on a system (e.g., Cloud and High-Performance Computing (HPC) system), the lack of a practical and efficient technique for data-loader allocation incurs GPU idleness and degrades the training throughput. Therefore, our work first focuses on investigating the impact of data-loading on the global training throughput. We then propose a throughput prediction model to predict the maximum throughput for an individual DDL training application. By leveraging the predicted results, A-Dloader is designed to dynamically allocate CPU and I/O resources to concurrently running DDL applications and use the data-loader allocation as a knob to reduce GPU idle intervals and thus improve the overall training throughput. We implement and evaluate A-Dloader in a DDL framework for a series of DDL applications arriving and completing across the runtime. Our experimental results show that A-Dloader can achieve a 28.9% throughput improvement and a 10% makespan improvement compared with allocating resources evenly across applications.

Teachers' Experiences in Implementing the School Literacy Movement (GLS): An Investigation of Junior High School Literacy Programs in Indonesia

This study explores the experiences of junior secondary school teachers in Indonesia who have implemented the School Literacy Movement (GLS). The study focuses on teachers' perceptions of GLS, its implementation, and its challenges. This study used a phenomenological approach involving 20 teachers to share their insights on the research topic. Data analysis was conducted through a qualitative research paradigm using ATLAS.ti 9 software. The tool facilitated the study of participants' transcripts and documentation, enabling the verbal expression of essential phrases or sentences, the identification of meanings and themes, and the combination of these elements into a comprehensive and insightful description. This study found that teachers implemented incomplete stages of GLS, driven by their limited perception of the program, which focused more on general reading skills. Their planning and implementation of GLS was superficial, and they faced significant issues such as time, evaluation, teacher workload, limited interaction or lack of training, and low student interest in reading. Challenges include limited learning resources and access and an unsupportive environment, facilities, and infrastructure

Impact of sports vision training on visuomotor skills and shooting performance in elite skeet shooters.

Vision serves as a critical channel for athletes to acquire information during competitions and constitutes a vital component of their competitive ability. Through scientifically designed sports visual training, specific visual skills can be enhanced, thereby assisting athletes in achieving optimal performance in competitive settings. This study aim to explore the visuomotor abilities and shooting performance of skeet shooters through Sports Vision Training (SVT). Twenty elite skeet shooters were recruited and randomly assigned to an experimental group (EXP, n = 10) and a control group (CON, n = 10). The EXP underwent 6-week of SVT on Senaptec Seneory Station, twice a week, while the CON completed an equivalent workload of target-tracking training. Visuomotor skills testing showed significant improvements in Near- Far Quickness, Perception Span, and Eye-hand Coordination in the EXP (p < 0.05), with no changes in the CON. Comparative post-test results between the two groups showed significant differences in N/F Q, Target Capture, Perception Span, Eye-hand Coordination, and Go/ No Go (p < 0.05). In shooting performance indicators, EXP shooters showed a highly significant improvement in hit accuracy (p < 0.01), with a similar difference compared to the CON. Additionally, they exhibited a highly significant improvement in shotgun-mounting reaction time (p < 0.01). Kinematic indicators of shotgun movement during the firing process for shot 2 showed significant differences in peak velocity (p < 0.01), X-axis (p = 0.033) and Y-axis (p = 0.001) displacement. SVT can enhance visuomotor abilities in skeet shooters and has a positive impact on their shooting technique. This is primarily manifested in shorter shotgun-mounting reaction time and improved efficiency in action at shot 2, effectively improving their shooting accuracy.

Tenascin-C-Matrix Metalloproteinase-3 Phenotype and the Risk of Tendinopathy in High-Performance Athletes: A Case-Control Study.

Background/Objectives: Tendon structure is predominantly composed of the extracellular matrix (ECM), and genetic variants in non-collagenous ECM components may influence susceptibility to tendinopathy. We investigated the potential influence of single nucleotide polymorphisms (SNPs) in fibrillin-2 (FBN2), tenascin-C (TNC), and matrix metalloproteinase-3 (MMP3) on the tendon regeneration failure phenotype and impact on the susceptibility to tendinopathy in Brazilian high-performance athletes. Methods: This case-control study was conducted with 397 high-performance athletes from different sports modalities (197 tendinopathy cases and 200 controls), and they were analyzed by validated TaqManTM SNP genotyping assays of the SNPs FBN2 (rs331079), TNC (rs2104772), and MMP3 (rs591058). Results: Out of the 197 tendinopathy cases, 63% suffered from chronic tendon pain and 22% experienced more than three episodes of disease manifestation. The TNC-rs2104772-A allele was significantly associated with tendinopathy (OR: 1.4; 95% CI: 1.1-1.8), while athletes carrying the MMP3-rs591058-T allele were linked to an increased risk of more episodes of disease manifestation (OR: 1.7; 95% CI: 1.1-2.8). The TNC-MMP3 tendon regeneration failure phenotype (TNC-A/MMP3-T) was associated with an increased risk of tendinopathy (OR: 1.4; 95% CI: 1.1-2.0) and episodes of disease manifestation (OR: 2.0; 95% CI: 1.2-3.5). Athletes with tendinopathy who had the TNC-A/MMP3-T interaction were more prone to experiencing more than three disease exacerbations (OR: 4.3; 95% CI: 1.8-10.5) compared to TNC-A/TNC-C. Conclusions: This study suggests that rs2104772 and rs591058 SNPs could be involved in the tendon regeneration failure phenotype and may influence the molecular mechanism related to the regulation of the tendon ECM during training workload.

Personal and Workplace Characteristics as Predictors of Intent-To-Stay Among Registered Nurses: An Exploratory Quantitative Multicentre Study.

This study aims to describe and examine the factors associated with registered nurses' intent-to-stay and subsequently identify predictors of nurses' intent-to-stay. A quantitative, cross-sectional correlational design was used. A convenience sample of 270 registered nurses completed the questionnaire and was included in this study. Descriptive statistics were used to present the sociodemographic characteristics and scores of outcome measures. Pearson's correlation coefficient and multiple linear regression with backward selection were conducted to examine how nurses' characteristics and workplace factors influence nurses' intent-to-stay. The mean age of the participants was 29.2 years. The mean scores for the outcomes were intent-to-stay (mean = 2.96), resilience (mean = 3.34), occupational self-efficacy (mean = 4.34), sleep quality (mean = 9.73) and workplace environment scores (mean = 3.15). The correlation analysis showed that resilience, occupational self-efficacy, self-realisation and workload were positively correlated to intent-to-stay while sleep quality was negatively correlated to intent-to-stay. Multiple linear regression analysis found occupational self-efficacy, sleep quality, workload, nervousness, nurses' designation and specialisation status to be significant factors associated with intent-to-stay. Intent-to-stay is a complex and multidimensional construct influenced by a variety of personal and workplace factors. Hospital administrators should endeavour to develop measures to improve occupational self-efficacy, workload, nervousness and push for specialisation training to bolster nurses' intent-to-stay. Against an everchanging healthcare landscape following the COVID-19 pandemic, the findings of this study contribute to a deeper understanding of the factors of registered nurses' intent-to-stay. The findings of this study alluded to the importance of professional development and workload as factors that can influence registered nurses' intent-to-stay. Hospital administrators can prioritise workforce retention policies by introducing strategies such as opportunities for upskilling, flexible working hours and streamlining work processes to promote nurses' intent-to-stay. No patient or public contribution.

Evaporated Copper‐Based Perovskite Dynamic Memristors for Reservoir Computing Systems

AbstractDynamic memristors are considered as the optimal hardware devices for reservoir computing (RC) enabled by their nonlinear conductance variations. This significantly reduces the extensive training workload typically required by traditional neural networks. Lead halide perovskites, with their tunable band structure and active ion migration properties, have emerged as highly promising materials for developing dynamic memristors. However, large‐scale and consistently stable production remains a challenge for perovskite functional films, while lead elements' toxicity and environmental impact also partly restrict their practical device utilization. In this work, lead‐free copper‐based perovskite (i.e., CsCu2I3) films are prepared by thermal evaporation for constructing dynamic memristors. The effective conductivity modulation of CsCu2I3‐based memristor can be utilized in artificial neural networks, achieving a high handwritten digit recognition accuracy of 91.2%. In addition, the RC system is also constructed based on the dynamic behavior of the devices, by which a letter recognition accuracy of 98.2% with simple training is achieved. This technology provides a feasible pathway to construct copper‐based perovskite dynamic memristors for future neural network information processing.

EcoFlow: Efficient Convolutional Dataflows on Low-Power Neural Network Accelerators

Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applications such as image segmentation and high-resolution image generation. We find that commonly-used low-power CNN inference accelerators are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">not</i> optimized for both these convolutional kernels. Dilated and transposed convolutions introduce significant zero padding when mapped to the underlying spatial architecture, significantly degrading performance and energy efficiency. Existing approaches that address this issue require significant design changes to the otherwise simple, efficient, and well-adopted architectures used to compute direct convolutions. To address this challenge, we propose EcoFlow, a new set of dataflows and mapping algorithms for dilated and transposed convolutions. These algorithms are tailored to execute efficiently on existing low-cost, small-scale spatial architectures and requires minimal changes to existing accelerators. At its core, EcoFlow eliminates zero padding through careful dataflow orchestration and data mapping tailored to the spatial architecture. We evaluate EcoFlow on CNN training workloads and Generative Adversarial Network (GAN) workloads. Experiments in our new cycle-accurate simulator show that, using a common CNN inference accelerator, EcoFlow 1) reduces end-to-end CNN training time between 7-85%, and 2) improves end-to-end GAN training performance between 29-42%, compared to state-of-the-art CNN dataflows.

Workloads in Collegiate Women's Lacrosse Athletes During a Division II National Championship Season.

Sutton, PJ, Mumford, PW, and Sunderland, KL. Workloads in collegiate women's lacrosse athletes during a Division II national championship season. J Strength Cond Res 38(9): 1651-1657, 2024-A comprehensive examination of the external and internal workloads in collegiate women's lacrosse athletes has yet to be reported. Thus, the primary purpose of this study was to determine the absolute and relative external and internal training and game workloads of National Collegiate Athletic Association (NCAA) Division II women's lacrosse athletes throughout an entire season. Data from 19 Division II women's lacrosse athletes were analyzed, encompassing each training session and game across an entire competitive season (February-May). External workloads were assessed using a wearable global positioning system, whereas internal workloads were determined through heart rate (HR) variables and session rating of perceived exertion. Game days were associated with significantly ( p < 0.05) greater absolute external and internal workloads. However, when comparing workloads relative to session duration, relative workloads between training and games were no longer significant ( p > 0.05) for total distance, high-speed running (≥15 km·h -1 ), HR-derived training impulse, or caloric expenditure. Nonetheless, relative sprint distance (>19 km·h -1 ) was significantly lower during games, whereas high-intensity accelerations (>2 m·s -2 ) and decelerations (<-2 m·s -2 ) were significantly greater during training compared with games ( p < 0.05). Practical applications of these findings suggest that coaches can better prepare athletes for game day conditions by adjusting training plans to replicate the duration and intensity of games. Overall, this comprehensive examination of internal and external workloads provides valuable data for coaches and practitioners to support performance comparisons, rehabilitation protocols, and workload analyses in collegiate women's lacrosse athletes.

Challenges in Implementing Extensive Reading in Thailand

This study explores the key challenges encountered in the implementation of extensive reading in the Thai context. The samples were 400 Thai students and teachers who were already familiar with the concept of ER. The participants' opinions, thoughts, and beliefs about the implementation of ER were also gathered through surveys and semi-structured interviews. The results reveal that these challenges include teachers’ beliefs about ER; insufficient support from schools and educational authorities; teachers' workload and lack of training; and students' motivation and reading habits. Overcoming these challenges requires collaborative efforts between educational institutions, administrators, teachers, and students to provide adequate resources, training, and a supportive environment to promote a reading culture in the EFL classroom in Thailand. Extensive reading programs (ERP) can be effectively implemented in Thailand by recognizing and resolving these issues, which will improve language learning outcomes and a lifelong motivation for reading among students.

PowerTrain: Fast, generalizable time and power prediction models to optimize DNN training on accelerated edges

Accelerated edge devices, like Nvidia’s Jetson with 1000+ CUDA cores, are increasingly used for DNN training and federated learning, rather than just for inferencing workloads. A unique feature of these compact devices is their fine-grained control over CPU, GPU, memory frequencies, and active CPU cores, which can limit their power envelope in a constrained setting while throttling the compute performance. Given this vast 10k+ parameter space, selecting a power mode for dynamically arriving training workloads to exploit power–performance trade-offs requires costly profiling for each new workload, or is done ad hoc. We propose PowerTrain, a transfer-learning approach to accurately predict the power and time that will be consumed when we train a given DNN workload (model + dataset) using any specified power mode (CPU/GPU/memory frequencies, core-count). It requires a one-time offline profiling of 1000s of power modes for a reference DNN workload on a single Jetson device (Orin AGX) to build Neural Network (NN) based prediction models for time and power. These NN models are subsequently transferred (retrained) for a new DNN workload, or even a different Jetson device, with minimal additional profiling of just 50 power modes to make accurate time and power predictions. These are then used to rapidly construct the Pareto front and select the optimal power mode for the new workload, e.g., to minimize training time while meeting a power limit. PowerTrain’s predictions are robust to new workloads, exhibiting a low MAPE of <6% for power and <15% for time on six new training workloads (MobileNet, YOLO, BERT, LSTM, etc.) for up to 4400 power modes, when transferred from a ResNet reference workload on Orin AGX. It is also resilient when transferred to two entirely new Jetson devices (Xavier AGX and Jetson Orin Nano) with prediction errors of <14.5% and <11%. These outperform baseline predictions by more than 10% and baseline optimizations by up to 45% on time and 88% on power.

Quantification of cardiac capillarization in basement-membrane-immunostained myocardial slices using Segment Anything Model

Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double immunostaining of cardiomyocytes (CMs) and capillaries in myocardial slices. In contrast, single immunostaining of basement membrane protein is a straightforward approach to simultaneously label CMs and capillaries, presenting fewer challenges in background staining. However, subsequent image analysis always requires expertise and laborious manual work to identify and segment CMs/capillaries. Here, we developed an image analysis tool, AutoQC, for automatic identification and segmentation of CMs and capillaries in immunofluorescence images of basement membrane. Commonly used capillarization-related measurements can be derived from segmentation results. By leveraging the power of a pre-trained segmentation model (Segment Anything Model, SAM) via prompt engineering, the training of AutoQC required only a small dataset with bounding box annotations instead of pixel-wise annotations. AutoQC outperformed SAM (without prompt engineering) and YOLOv8-Seg, a state-of-the-art instance segmentation model, in both instance segmentation and capillarization assessment. Thus, AutoQC, featuring a weakly supervised algorithm, enables automatic segmentation and high-throughput, high-accuracy capillarization assessment in basement-membrane-immunostained myocardial slices. This approach reduces the training workload and eliminates the need for manual image analysis once AutoQC is trained.

Challenges and Opportunities for Special-Needs Children in Elementary Schools

This research explores the challenges and opportunities for inclusive education for special-needs children in elementary schools in Indonesia. It examines the impact of government policies, the actual conditions in the field, and the availability of supporting facilities and resources. The study utilizes a qualitative method with a case study approach, involving various stakeholders such as special-needs children, teachers, parents, and school principals as informants. Findings indicate that while government policies have improved access to education for special-needs children, there are disparities in implementation and resource allocation among schools. Teachers face obstacles due to a lack of specialized training and high workloads, while limited facilities and stigma also hinder inclusive education. The research emphasizes the need for continuous support, changes in community attitudes, and improvements in teacher training and facilities to ensure effective inclusive education for special-needs children in elementary schools.

Progressive overload in cardiorespiratory exercise training for young and old: Is increasing duration or intensity of exercise more important?

People undertake endurance training to improve their health and cardio-respiratory fitness. Subsequent cardio-respiratory and skeletal muscle aerobic metabolic adaptations are gauged by improvements in maximum or peak oxygen uptake (V̇O2peak) and the blood lactate inflection point or threshold during incremental exercise – (LIP). To further improve physiological capability, subsequent homoeostatic disturbances from exercise should be progressively greater in succeeding exercise sessions. Therefore, exercise duration, frequency or intensity should be progressively increased during a training regimen to ensure adaptation potential is realized. A progressive increase in training workload is termed “progressive overload”. Despite the universal acknowledgment of the importance of progressive overload, it is unclear if systematically progressing altering either exercise intensity or duration (or distance covered) affect cardio-respiratory gains differently in young and older populations. As running faster results in higher heart rates and greater skeletal muscle metabolic stress than running the same distance at a lower speed, in this hypothesis we postulate that progressively increasing exercise run intensity will result in greater mean and higher incidence of V̇O2peak and LIP gains in young adults (<50 years). However, the mechanisms that initiate improvements in cardio-respiratory fitness and skeletal muscle aerobic function may be different in older adults due to the inevitable aging decline in cardio-vascular function and mechanical and morphological properties of muscle–tendon units. In older adults (>60 years) we hypothesize progressively increasing run distance while maintaining the same speed will just be as effective as to progressively increasing speed to improve V̇O2peak and LIP. To test these hypotheses, we propose a study that compares progressively increasing run intensity to a treatment of progressive matched run distance where speed remains constant in young and old adults.

Competitive profile analysis according to playing positions of female handball players during the European Championship 2020

ABSTRACT Handball is one of the most popular team sports and requires the correct analysis of players’ physical condition for its development, especially in women´s handball. Therefore, the aim of this study was to analyse for the first time the competitive demands of professional handball players during the EHF Championship 2020, using the innovative Local Positioning System (LPS) technology. In order to achieve this goal, 231 female players from 16 national teams were analysed during 47 matches using LPS and a specific handball analytic system developed for this purpose. Players covered more distance in the offensive phase (1205.6 ± 636.7 m) and in all locomotion categories, with the exception of high intensity running, than in the defensive phase (834.2 ± 471.5 m), with a similar average total time on court. The running pace was significantly higher in attack (56.0 ± 26.2 m/min) than in defence (39.2 ± 19.8 m/min). Regarding playing positions, wing players covered higher distances in the high intensity categories than the rest of the playing positions. It could be concluded that the training workload of elite handball players should be programmed according to specific positions.

Harnessing Manycore Processors with Distributed Memory for Accelerated Training of Sparse and Recurrent Models

Current AI training infrastructure is dominated by single instruction multiple data (SIMD) and systolic array architectures, such as Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), that excel at accelerating parallel workloads and dense vector matrix multiplications. Potentially more efficient neural network models utilizing sparsity and recurrence cannot leverage the full power of SIMD processor and are thus at a severe disadvantage compared to today's prominent parallel architectures like Transformers and CNNs, thereby hindering the path towards more sustainable AI. To overcome this limitation, we explore sparse and recurrent model training on a massively parallel multiple instruction multiple data (MIMD) architecture with distributed local memory. We implement a training routine based on backpropagation though time (BPTT) for the brain-inspired class of Spiking Neural Networks (SNNs) that feature binary sparse activations. We observe a massive advantage in using sparse activation tensors with a MIMD processor, the Intelligence Processing Unit (IPU) compared to GPUs. On training workloads, our results demonstrate 5-10x throughput gains compared to A100 GPUs and up to 38x gains for higher levels of activation sparsity, without a significant slowdown in training convergence or reduction in final model performance. Furthermore, our results show highly promising trends for both single and multi IPU configurations as we scale up to larger model sizes. Our work paves the way towards more efficient, non-standard models via AI training hardware beyond GPUs, and competitive large scale SNN models.

Wearable Devices and Digital Biomarkers for Optimizing Training Tolerances and Athlete Performance: A Case Study of a National Collegiate Athletic Association Division III Soccer Team over a One-Year Period.

Wearable devices in sports have been used at the professional and higher collegiate levels, but not much research has been conducted at lower collegiate division levels. The objective of this retrospective study was to gather big data using the Catapult wearable technology, develop an algorithm for musculoskeletal modeling, and longitudinally determine the workloads of male college soccer (football) athletes at the Division III (DIII) level over the course of a 12-week season. The results showed that over the course of a season, (1) the average match workload (432 ± 47.7) was 1.5× greater than the average training workload (252.9 ± 23.3) for all positions, (2) the forward position showed the lowest workloads throughout the season, and (3) the highest mean workload was in week 8 (370.1 ± 177.2), while the lowest was in week 4 (219.1 ± 26.4). These results provide the impetus to enable the interoperability of data gathered from wearable devices into data management systems for optimizing performance and health.

Injuries in the emerging sport of roundnet (Spikeball): a cross-sectional study of 166 players

ObjectivesTo describe injury frequency and characteristics in roundnet athletes and compare injury characteristics between elite and non-elite athletes.MethodsThis cross-sectional study was performed by convenience sampling recreational and competitive roundnet athletes...

Pressure Ulcer Preventive Measures Utilized and Its Perceived Effectiveness Among Nurses in Selected Hospitals in Benin City, Edo State, Nigeria

Background: Pressure ulcers represent a largely preventable patient safety issue, serving as an indicator of nursing care quality and patient safety within healthcare settings. This study aimed to assess the utilization and effectiveness of pressure ulcer preventive measures among nurses in selected hospitals.&#x0D; Materials and Methods: This descriptive cross-sectional survey involved 304 nurses purposefully selected from surgical wards/units in the chosen hospitals. A well-structured, pretested questionnaire with a reliability index of 0.842 was employed for data collection. Descriptive statistics, including mean, frequency, and percentage, were used for data analysis.&#x0D; Results: Findings indicated that risk assessment (mean 3.67±0.56) was the most frequently used preventive measure, followed by skin repositioning every 2 hours (3.46±0.71), use of barrier creams (3.35±0.71), and the utilization of pressure-relieving devices such as air beds (3.33±0.75). Prophylactic sacral dressing (3.14±0.76) and massaging of bony prominences (3.17±0.86) were also commonly employed. The most effective measures were risk assessment (mean 2.66±0.5), repositioning every 2 hours (2.56±0.54), and skin assessment (2.54±0.54). Barriers to preventive measures included limited equipment (3.31±0.96), nursing shortages (3.12±0.77), lack of skill/training (3.09±0.85), and heavy workloads (3.14±0.82).&#x0D; Conclusion And Recommendation: This study highlights that risk assessment was the most used and effective preventive measure among nurses. Challenges, such as the lack of training and heavy workloads, hindered utilization. Recommendations include regular updates on best practices for staff, especially new nurses, to bridge the theory-practice gap and promptly implement research findings.