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A sustainable career path for cancer survivors returning to work : new theorising from an inductive qualitative case study
No full textThis study aims to understand the workplace challenges cancer survivors face when they return to work, and to analyse what human resources management (HRM) professionals and line managers can do to protect and motivate these survivors. This article opens with a review of the literature on cancer survivors and work, from an HRM perspective. A qualitative case study approach is adopted to understand the experiences of and challenges faced by cancer survivors returning to work and human resource (HR) managers’ actions to address these challenges. The outcomes of this research are translated into a set of sustainable HRM practices for cancer survivors (HR planning, job design, career development, compensation, performance evaluation and training), and guidance in the form of proposals for management and government agencies to regulate the experience of returning to work
Continuous video recording with simultaneous amplitude-integrated EEG monitoring to improve seizure recognition in newborns
No full textSeizures in newborn infants are considered a neurological emergency requiring prompt treatment to limit exacerbation of brain injury. Digital monitors incorporating limited channel raw EEG and amplitude-integrated EEG (aEEG/EEG) are widely used. This study aimed to determine if continuous video recording with simultaneous aEEG/EEG recording enhances seizure recognition. Newborns at risk of seizures who underwent neuromonitoring with aEEG/EEG were prospectively recruited to an observational study in a tertiary neonatal centre. Video recordings were commenced after obtaining written consent from parents. Simultaneous video recordings with aEEG/EEG were obtained in 15/47 newborns recruited to study. A total of 116 electrographic seizure episodes were detected on aEEG/EEG when a total of 56 episodes of abnormal movements were noted on video recordings. Only 8 of these abnormal movements had simultaneous electrographic seizures on aEEG. Use of simultaneous video and aEEG/EEG recordings in newborns at risk of seizures is feasible. It not only assists confirmation of the presence of seizures but may also help in identifying movements associated with abnormal neurology that are not seizures
Leveraging AI for strategic foresight : unveiling future horizons
No full textThis article explores how Artificial Intelligence (AI) can revolutionize strategic foresight, the systematic exploration of potential futures. The unparalleled pace of change necessitates robust foresight capabilities to navigate uncertainties and shape the future. Traditional foresight techniques, while valuable, can be limited by data processing and the challenge of modelling complex causal relationships the transformative influence of Artificial Intelligence (AI) in enhancing strategic foresight capabilities, guiding decision-makers through an ever-changing landscape to navigate uncertainties and shape the future. The chapter highlights the potential of emerging deep learning AI techniques, such as causal and Generative AI, to enhance traditional foresight approaches by bridging the gap between quantitative and qualitative foresight methods, addressing challenges such as expert bias and data reliability. Causal AI harnesses the power of causal inference to model complex causal relationships and mitigate observational bias in data, equipping decision-makers with robust tools for scenario building. Generative AI and LLMs on the other hand, offer promising capabilities for automated horizon scanning and scenario generation, although the need for human oversight remains Ultimately, AI could empower individuals and organizations to not only anticipate the future but actively shape it
Comparison of glycosylated fibronectin versus soluble fms-like tyrosine kinase/placental growth factor ratio testing for the assessment of pre-eclampsia : protocol for a multicentre diagnostic test accuracy study
Full text linkAir pollution monitoring using cost-effective devices enhanced by machine learning
Full text linkGiven the significant impact of air pollution on global health, the continuous and precise monitoring of air quality in all populated environments is crucial. Unfortunately, even in the most developed economies, current air quality monitoring networks are largely inadequate. The high cost of monitoring stations has been identified as a key barrier to widespread coverage, making cost-effective air quality monitoring devices a potential game changer. However, the accuracy of the measurements obtained from low-cost sensors is affected by many factors, including gas cross-sensitivity, environmental conditions, and production inconsistencies. Fortunately, machine learning models can capture complex interdependent relationships in sensor responses and thus can enhance their readings and sensor accuracy. After gathering measurements from cost-effective air pollution monitoring devices placed alongside a reference station, the data were used to train such models. Assessments of their performance showed that models tailored to individual sensor units greatly improved measurement accuracy, boosting their correlation with reference-grade instruments by up to 10%. Nonetheless, this research also revealed that inconsistencies in the performance of similar sensor units can prevent the creation of a unified correction model for a given sensor type
Real-like synthetic sperm video generation from learned behaviors
No full textComputer-assisted sperm analysis is an open research problem, and a main challenge is how to test its performance. Deep learning techniques have boosted computer vision tasks to human-level accuracy, when sufficiently large labeled datasets were provided. However, when it comes to sperm (either human or not) there is lack of sufficient large datasets for training and testing deep learning systems. In this paper we propose a solution that provides access to countless fully annotated and realistic synthetic video sequences of sperm. Specifically, we introduce a parametric model of a spermatozoon, which is animated along a video sequence using a denoising diffusion probabilistic model. The resulting videos are then rendered with a photo-realistic appearance via a style transfer procedure using a CycleGAN. We validate our synthetic dataset by training a deep object detection model on it, achieving state-of-the-art performance once validated on real data. Additionally, an evaluation of the generated sequences revealed that the behavior of the synthetically generated spermatozoa closely resembles that of real ones
"My boss makes the most out of it" : the predictive value of learning climates for employability
No full textThis study aims to examine to what extent the employability of followers and their managers is equally supported by organizational learning climates. Studies often assume that managers and their followers benefit equally from these climates. However, this assumption overlooks the distinct roles and positions that managers hold in comparison with their followers. Managers typically have more freedom to engage in learning activities, make decisions about their professional development and leverage organizational resources to support their growth. Consequently, they may have better positions to reap the benefits of learning climates than followers, whose roles may be constrained by organizational hierarchies. Using an actor–partner interdependence model, in a dyadic study among 205 manager-follower dyads, we investigated how three specific learning climates—appreciation, facilitation and error avoidance—relate to managers' and followers' employability. Our findings revealed that managers' employability benefits from all three climates. Contrastingly, followers' employability is enhanced only by a facilitating learning climate. These results suggest that learning climates primarily enhance managers' career potential, while followers depend more on direct facilitation to improve their employability
The jury is in : an evaluation of an experiential court assignment
No full textThis study evaluates the effectiveness of an experiential learning assignment designed for criminology and forensic psychology students, requiring them to attend a Crown Court trial in the public gallery or to engage with a virtual mock trial. 48 students were surveyed to measure the impact of experiential assignments in helping students better understand the module content, the criminal justice system and if the experience increased their motivation to continue with their course. Findings indicate strong student support for the assignment, with 81.3% stating it as valuable and 79.2% wanting more experiential learning opportunities in their criminal justice related courses. We found that in-person experiences received slightly higher student ratings, however, both in-person and virtual contributed positively to learning outcomes. We highlight the importance of experiential learning in improving student engagement, and real-world application of their degree
