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Academic readiness to teach electronic medical records education into pre-registration nursing programs. An integrative review
No full textDigital health technologies, such as Electronic Medical Records (EMR) are seen as the preferred method for patient documentation in many healthcare settings (Australian nursing and midwifery accreditation council registered nurse accreditation standards, 2019; Mollart et al., 2023). Electronic patient record systems, also known as electronic health records (EHR) or EMR can be defined as a repository of patient data that is stored in a secured electronic form, which can be accessed by multiple authorised users (Elliott et al., 2018). EMRs have been implemented globally into healthcare facilities for over a decade to assist in the reduction of patient documentation errors, improve communication amongst the multi-disciplinary team, and to improve patient safety (Wilbanks et al., 2018)
A Scoping Review of the Barriers and Facilitators to Implementing Small-Scale Residential Dementia Care
No full textBackgroundThere is a need to understand the benefits and limitations of innovative models of dementia care to ensure models meet the needs of people living with dementia, their families and staff. The aim of this scoping review was to explore and synthesise the barriers and facilitators to the widespread implementation of small-scale residential dementia care.MethodA scoping review was conducted in 2023 in MEDLINE, CINAHL, PsycINFO, Scopus, Web of Science, and CENTRAL to identify empirical, peer-reviewed studies, published in English from database inception to October 2023. Studies of small-scale residential dementia care with 6-15 people were included.ResultForty-four studies were identified and synthesised into five focus areas for investigation: People with dementia (n = 19), families (n = 2), staff (n = 12), the built environment (n = 8) and other studies with a focus on a program of care or studies with two or more study populations (n = 3). Studies were published between 1990 and 2023 and included quantitative (n = 29), qualitative (n = 13) and mixed methods (n = 2) approaches. Multiple barriers and facilitators were identified in each focus area that either support or limit the widespread implementation of small-scale residential dementia care. A key facilitator is the presence of a clear philosophy of small-scale care that guides the planning of a small-scale setting or adaptation of a traditional care setting to incorporate principles of small-scale care. The philosophy should be articulated through policies, communicated to stakeholder groups and implemented consistently in the delivery of care. A key barrier relates to the ability of the care organisation to adjust staff roles to deliver small-scale care. With broader caring roles and greater decision-making authority, organisational changes are required to staff ratios, training and education to empower staff to deliver this care.ConclusionSmall-scale residential dementia care has the potential to benefit people with dementia, their families, the staff and the wider community. Through the establishment of a clear philosophy of small-scale care, there is an opportunity to place the person with dementia at the centre of care, provide access to meaningful and appropriate activities of daily living, create a home-like environment and prioritise relationship building at all levels
What is news in a high-choice media environment? An adapted boundary framework for audience definitions of news
Get PDFIn a choice-rich media environment, audience perceptions of what is and is not news are not static. However, most of the scholarship about news definitions has been from the perspective of the practitioner. This article takes an audience-centric approach. Based on thematic analysis of 60 semi-structured interviews with Australians, it finds traditional understandings of news persist for many, but broader definitions outside of traditional conceptions of news are more likely to be embraced by under-35 and non-news consuming audiences. Using an adaptation of Gieryn’s boundary framework and its dimensions of expansion, expulsion, and protection, the analysis revealed strong sentiments about the quality of mainstream news from traditional news adherents, those who perceive expanded news boundaries, and those who reject legacy news. The evidence provided, and the new conceptual framework can assist news organisations, policymakers and scholars in their understanding of low and non-news users’ engagement with and perception of news.</p
Nurses’ and midwives’ job satisfaction and retention during COVID-19: a scoping review
No full textBackgroundThe COVID-19 pandemic focused attention on the previously well-documented world-wide shortage of nurses and midwives. The pandemic accentuated this crisis, which resulted in nurses and midwives questioning their roles and their careers. The impact of the pandemic on job satisfaction and the intention of nurses and midwives to stay has not been adequately explored.AimThe aim of this scoping review was to identify and map the literature that describes the intrinsic and extrinsic factors that influenced nurses’ and midwives’ job satisfaction and intent to stay or leave their employment or profession during the COVID-19 pandemic.MethodThis scoping review was conducted according to the Preferred Reporting Items for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) guidelines. Studies written in English and published between 2019 and December 2023 were included, and consisted of quantitative, qualitative and mixed methods studies. MEDLINE, CINAHL, and PsychINFO were the key information sources. The search terms for this review were developed using the PCC mnemonic: Population, Concept and Context. The JBI approach to sources selection, data extraction and presentation of data was used.ResultsA total of 1833 relevant articles were identified. Of these, 17 articles met the inclusion criteria. Ten factors were identified as impacting on nurses and midwives job satisfaction and retention during the COVID-19 pandemic.ConclusionThe findings reveal key factors, such as patient acuity, staffing dynamics, leadership quality, and resource availability, have significantly shaped the professional experiences of nurses and midwives during the pandemic. These insights can inform targeted policies and interventions to improve job satisfaction and retention, while future research should address gaps, particularly the unique challenges faced by midwives, to strengthen the healthcare workforce for future crises
Washpool
No full textA magical middle-grade adventure about two sisters who need to rely on their own wits and each other when they're pulled into a new world, from black&write! fellow and bestselling author, Lisa Fuller.This world looked as though it had been drawn in weird crayon colours ... There were no bird calls. No distant rustling of animals in the scrub. No breeze teasing the tops of the trees. Everything was still.Bella is shy and thoughtful. Her big sister, Cienna, is popular and brave. One thing they have in common is their love for Washpool, the local swimming spot. But one weekend when they dive into Washpool, Bella and Cienna surface in the strange new world of Muse.Lost and confused, the sisters find themselves working with magical creatures on an eye-opening quest. Lady Dragon - keeper of the land where an important Summer Feast is held - has lost her egg. Can Bella and Cienna save the egg in time? And how will they ever find their way home?A transportive middle-grade fantasy adventure
Investigating the effectiveness of hybrid gradient boosting models and optimization algorithms for concrete strength prediction
No full textThis study aims to evaluate and predict the compressive strength of concrete using 8 different machine learning (ML) models, including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LightGBM), Gradient Boosting with Categorical features support (CatBoost), Gradient Boosting Regressor (GBR), Adaptive Boosting (AdaBoost), Decision Tree (DT), Random Forest (RF), and Support Vector Machine Regression (SVR). The study employed Bayesian optimisation process with two surrogate models (Gaussian Processes and Random Forest) and Random Search optimisation process to optimise the hyperparameters of these ML models. 1030 data samples were used to train the models and analyse the feature importance of each input variable using SHapley Additive exPlanations (SHAP). The results indicated that all 8 hybrid ML models performed well with R2 values larger than 0.80 and four models (XGBoost, CatBoost, GBR, and LightGBM) being the standout models, achieving R2 values of 0.94, 0.94, 0.92, and 0.92 on testing dataset, respectively. The four leading models (XGBoost, CatBoost, GBR, LightGBM) were applied to six sub-databases of concrete types, significantly enhancing accuracy with all models achieving R2 values over 0.98 on the testing dataset. The study also found that curing age, cement content, and amount of water were the most important variables affecting compressive strength while fly ash was the least important. By deploying the three best models to the cloud, it is now possible to make predictions using any web browser on any device.</p
Changing everyday pedagogical practices with digital technologies
No full textAdapting or changing pedagogical practices to incorporate digital technologies is notoriously challenging in early childhood education and care (ECEC). While digital technologies are part of the everyday lives of many children and educators, some educators remain concerned about too much screen time, the appropriateness of using digital technologies in ECEC settings, and how to integrate digital technologies to enhance learning for young children. This chapter explores some of the approaches that educators adopted when using technologies in the classroom, and some of the opportunities for children’s learning that occurred. It describes experiences that educators trialled when engaging children with digital technologies and identifies and explains how technologies can be used to enhance children’s play, including the practices of acknowledging, interpreting, and integrating. Examples of how children engaged with digital technologies reflect imaginative encounters where learning opportunities were plentiful. The results of an environmental scan (adapted from the Inventory for Early Years Settings – see Marsh et al., 2005) in classrooms at three timepoints depicted how educators made changes in their use of digital technologies.</p
Early-stage Parkinson's disease detection using multimodal brain-body biomarkers from fNIRS and IMU data
No full textParkinson's Disease (PD) is a progressive neurodegenerative disorder that impairs both motor and cognitive functions. Accurate detection of PD remains a major challenge, particularly at early stages when clinical symptoms are subtle. This study presents the first multimodal machine learning framework integrating functional near-infrared spectroscopy (fNIRS) and inertial measurement unit (IMU) data for early-stage PD detection during dual-task mobility assessments. Data were collected from 62 participants, including 28 people with PD and 34 age-matched controls, who performed the clinically recommended Timed Up and Go (TUG), Cognitive Dual-Task TUG (CDTUG), and Motor Dual-Task TUG (MDTUG) tests. This complex multimodal experimental design simultaneously captured brain activation and body motion under motor and cognitive dual-task conditions. Four machine learning models combined with two feature selection techniques were applied to unimodal and multimodal datasets. The multimodal approach achieved superior classification accuracy (96%) compared to fNIRS-only (87%) and IMU-only (95%) models. Key brain-body biomarkers were identified, including dorsolateral prefrontal and frontopolar cortex activations during dual tasks, alongside motor features such as turn, sit-to-stand, and stand-to-sit durations. These findings highlight the promise of combining brain and motion measures and complex functional mobility tests for early-stage PD detection and advance the development of non-invasive, AI-driven biomarker discovery frameworks.</p
Objective physical activity in people with young onset dementia, late onset dementia, and without dementia:A UK Biobank study
No full textBackgroundCurrent physical activity literature does not distinguish between young (dementia diagnosed before 65) and late onset dementia despite differences between these groups such as age, being known to influence physical activity levels.ObjectiveThe primary aim was to compare objective physical activity levels between people with young onset dementia, late onset dementia, and age-matched control participants without dementia.MethodsThis cross-sectional analysis included four groups (young onset dementia [n = 23]; young onset control [n = 782]; late onset dementia [n = 30]; late onset control [n = 918]) of participants aged 49 to 76 (56% male) from the UK Biobank. Objective light intensity physical activity, moderate-vigorous intensity physical activity, sedentary behavior, and sleep were measured using 7-day wrist-worn accelerometry.ResultsPeople with young onset dementia did more light and moderate-vigorous intensity physical activity than those with late onset dementia, with these differences becoming nonsignificant when controlling for age. There were no significant differences between people with young onset dementia and the young onset control group. Comparatively, people with late onset dementia did less light intensity physical activity and spent more time sedentary and sleeping than the late onset control group.ConclusionsThis study highlights the distinct physical activity levels of people with young onset and late onset dementia. Future physical activity research should distinguish between young onset and late onset dementia. Such an approach will be important for producing findings that are more applicable for individuals diagnosed with dementia at all stages of life.</p