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Introduction to the Special Issue on Judgment in Forecasting
No full textOne of the critical challenges in forecasting relates to the question of how to effectively use human judgment to optimize predictive performance, judgment permeating all aspects of the forecasting process whatever the application area (e.g., Bolger & Harvey, 1998). While past research on the role of judgment in forecasting has frequently focused either on studying the predictive performance of unaided judgmental predictions or on the role of judgmental adjustments of (linear) model forecasts, there is an emerging necessity to understand the relevance of human intervention more broadly when designing and implementing forecasting systems...</p
The relationship between workplace learning and employee satisfaction in UAE healthcare professionals
No full textThis research explores the mediating role of motivation between workplace learning and employee job satisfaction of doctors in the UAE’s healthcare organisations. This research uses an explanatory mixed-method research design where qualitative and quantitative methods elicit the necessary responses in addressing the topic under study. A survey using a questionnaire with 150 doctors, and interviews with 12 doctors, were conducted to determine their perceptions about workplace learning, motivation, and employee satisfaction. The questionnaire employed a 5-point Likert scale and questions were developed from pre-existing scales to determine the relationships between workplace learning, motivation, and employee job satisfaction empirically. The interviews were conducted to obtain detailed perceptions from doctors about how they feel motivated and satisfied and engaged in learning practices. The quantitative analysis was done using descriptive analysis, correlation analysis, and regression analysis to determine the mediating effect of motivation on the relationship between workplace learning and employee job satisfaction. The findings showed that workplace learning is positively correlated with job satisfaction, with motivational conditions mediating the relationship. In particular, perceived motivation about the learning environment is also positively correlated with job satisfaction, where job satisfaction was defined as doctors’ overall perceived satisfaction with their jobs. This study reveals that organisational supportive policies of workplace learning alongside motivational support enhance job satisfaction. This brings the debate on training doctors at the workplace in tune with motivational antecedents, such as recognition, development opportunities and incentives to improve satisfaction and engagement among the doctors. Thematic analysis conducted for qualitative data also led to the development of four themes, formal workplace learning, informal workplace learning, general employee engagement and development, and impact of workplace learning on job satisfaction. In addition, speciality training courses can be formulated as per the doctors’ career paths and motivation to improve satisfaction among doctors. Healthcare policymakers and administrators could also use these findings to influence the design of professional development programs and integrate motivational factors in work-related learning to enhance employee satisfaction and, consequently, healthcare delivery.</p
Political “color” and the impact of climate risks on output growth: evidence from a panel of US states
No full textIn this paper, we show that the effect of climate risks on economic growth in a panel of 48 contiguous states of the US is contingent on the party affiliation of the local politicians, as captured by a Democratic-Republican Index (DRI). Specifically, our results, based on a regime-dependent local projections model, indicate that extreme weather-related shocks tend to negatively impact output growth more severely, especially in the medium- to long-run, in the Republican-leaning states with low-DRI values compared to those characterized by high-DRIs over the annual period 1967 - 2023. In addition, when we incorporate the information on states that have undertaken explicit targets for reduction of greenhouse gas emissions, following the Climate Change Action Plan implemented in 1993, we find that the significant long-horizon negative effect continues to hold only for the states with low-DRIs, i.e., those that are Republicans-oriented.</p
An AI-based microsimulation for predicting health outcomes among people experiencing homelessness
No full textBackground and objective: People experiencing homelessness (PEH) face higher cancer risk due to social exclusion, housing and limited access to healthcare. This study proposes a microsimulation model using machine learning (ML) to predict the effect of quality of life, healthcare utilisation and empowerment at the end of the intervention under the Health Navigator Model, enabling cost-effective resource allocation and identifying high-risk subgroups.Materials & Methods: We used data from 652 PEH recruited in four European countries (June 2022 – November 2023); 255 completed an 18-month Health Navigator Model programme. Standardised questionnaires were administered at baseline, four weeks and post-intervention. A modular ML microsimulation was built that (1) creates a constraint-based synthetic cohort, (2) estimates outcome changes by matching each simulated case to real program completers, and (3) sums those differences to gauge the intervention’s impact. Multiple ML techniques were tested to keep the synthetic sample true to the original and to improve effect-size predictions.Results: CTGAN generated the most realistic synthetic baseline (propensity score = 0.152; 95 % CI 0.148–0.162), markedly outperforming univariant, multivariant and SMOTE approaches (> 0.21). Regression models reproduced most numerical outcomes with good fidelity (e.g., EQ-5D-5L MAE = 0.10 on a 0–1 scale; Health-Rating MAE = 10 on a 0–100 scale), while categorical outcomes were predicted within roughly one category. Binary classifiers yielded F1-scores of 0.58 for smoking status and 0.64 for programme adherence. An online demonstrator (https://epione.upv.es) visualises the process.Conclusion: The proposed ML-based microsimulation generates realistic PEH profiles and projects intervention outcomes, providing a flexible, evidence-driven tool to optimise cancer-prevention strategies for PEH supporting evidence-based decision-making and optimise resource allocation, enhancing intervention outcomes by predicting the intervention before implementation.</p
Thematic Assessment Report on the Underlying Causes of Biodiversity Loss and the Determinants of Transformative Change and Options for Achieving the 2050 Vision for Biodiversity of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Transformative Change Assessment).
No full textSummary for Policymakers of the Thematic Assessment Report on the Underlying Causes of Biodiversity Loss and the Determinants of Transformative Change and Options for Achieving the 2050 Vision for Biodiversity of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services.The IPBES Transformative Change Assessment was initiated following a decision from the IPBES Plenary at its eigth session (IPBES-8, June 2021), and has been approved by the IPBES Plenary at its eleventh session (IPBES-11, Windhoek, 2024). It is composed of a Summary for Policymakers and five Chapters.IPBES is an independent intergovernmental body established by Governments in 2012, IPBES provides policymakers with objective scientific assessments about the state of knowledge regarding the planet’s biodiversity, ecosystems and the contributions they make to people, as well as options and actions to protect and sustainably use these vital natural assets.</p
The validity and reliability of two golf performance-tracking global positioning system (GPS) devices
No full textThe validity and reliability of two golf performance-tracking 1-Hz global positioning system (GPS) devices for measuring shot distance, Arccos and Shot Scope CONNEX, was assessed. Ten golfers participated in an 18-hole validity condition (device vs. a laser rangefinder/tape measure control), and a 9-hole reliability condition assessing interunit differences in shot distance. Arccos and Shot Scope displayed high levels of validity with strong Spearman rank-order correlation coefficients (r ≥0.91, p <0.05), and a mean bias of ≤1 yard across all distance categories. Both systems appeared highly reliable with strong Spearman rank-order and intraclass correlation coefficients (r ≥0.84, p <0.05) and low typical error (Shot Scope: ≤2 yards; Arccos: ≤3 yards) across all distance categories. Arccos did not detect 67 shots, whilst Shot Scope did not detect 3. Arccos and Shot Scope appear highly valid and reliable methods of measuring golf shot distance and afford golf practitioners and researchers the opportunity to confidently measure on-course golf performance.</p
A mixed-methods investigation into positive body image in autistic individuals
No full textPositive body image is a multifaceted construct broadly defined as “an overarching love and respect for the body”. Accumulating research has explored this construct among historically marginalised populations (i.e., identity groups who experience discrimination and exclusion), identifying both commonalities and unique experiences. However, one group that remains largely unexamined is the autistic population. Using an exploratory sequential, mixed-methods design, the overarching aim of this thesis was to develop a theory of positive body image in autistic adults from the United Kingdom. In Study 1, I developed a grounded theory of positive body image with 20 autistic individuals using a qualitative framework. In Study 2, I used my qualitative findings to develop a revised version of the Body Appreciation Scale-2 – the BAS-2A – and examined its factorial stability and psychometric properties in 550 autistic adults. In Study 3, I tested the measurement invariance of the BAS-2A among subgroups of autistic adults across sexual identity (n = 376) and disability status (n = 378). In Study 4, I investigated direct associations between autistic traits and body appreciation in a sample of 530 autistic adults. Finally, in Study 5, I tested a mediational model whereby the association between positive autistic identity and flourishing is mediated by body appreciation in autistic adults (N = 384). Overall, my findings indicate that positive body image in autistic adults involves appreciating and respecting one's autistic body across its aesthetic, functional, and sensory aspects. To capture this construct, my findings support a revised 12-item BAS-2A as a psychometrically robust measure of body appreciation for use in autistic adults. In addition, my results showed that masking and alexithymia are negatively associated with body appreciation. Finally, body appreciation significantly and positively mediated the association between positive autistic identity and flourishing. While aspects of autistic people’s body appreciation accord with existing theory, I found multiple autism-specific components which require further investigation in the wider autistic population.</p
RadioMe: an automated home-based radio, music playlist, and diary reminder system: report on recruitment, music compilation, and listening, and preliminary testing of heart rate activated music
No full textBackground: One of the leading reasons for early admission to a care home in dementia is the escalation of neuropsychiatric symptoms (NPS), and music listening can help regulate these symptoms.Aims: RadioMe was a project designed for people living at home with dementia to build a system to help them maintain the highest quality of life there for as long as possible, with three functional components: 1. Streaming radio; 2. Providing pre-recorded spoken diary reminders; 3. interrupting the radio with pre-compiled playlists when a wrist-worn heart rate (HR) monitor detects stress. This article reports on the first two stages of this three-stage project: 1. recruitment, the music compilation process, responses of participants when listening, collection of daily agitation HR and behavioural data, and 2. preliminary testing of HR-activated music. Materials and methods: In stage 1, a playlist compilation process was co-designed with a lived experience group; HR and behavioural data were collected by participants when agitated to refine the algorithm used for automated music activation; 15 home visits were conducted to compile and test the playlists, collecting video, HR, and autobiographical data in each session to inform on playlist suitability for NPS management. Stage 2 involved installing systems to test automated playlist activation, and informal feedback was gathered on system functionality and user experience. Findings: The music compilation process enabled the creation of bespoke playlists. Sessional HR and video data had limited utility in supporting the suitability of music for NPS management. The methodology for collecting agitation data from participants failed, and the algorithm was not refined. Researchers compiled playlists with 25 people living with dementia, with a mean age of 73.8 years ( n = 12 men, 13 women). Ten participants had systems installed to test automated music activation. They found it too complex; system calibration was not sensitive enough, music played at random times, and it became repetitive. The system needs extensive refinement to simplify its operation. The activation of the music needs to be better calibrated. A feasible and effective method of gathering data from participants in their homes is required to refine the algorithm, which must include HR/biodata during milder NPS events, as participants reported these to be more in line with their symptoms.</p
Comorbidity management in nursing practice: managing mental health and physical health needs
No full textThis article presents a critical evaluation of the nursing interventions for managing comorbidity in patients: paranoid schizophrenia and type 2 diabetes mellitus. Paranoid schizophrenia and type 2 diabetes mellitus were assessed using validated tools and the article critically discusses evidence-based nursing interventions. The article discusses the potential risk of developing type 2 diabetes mellitus based on some antipsychotic medications and mitigating interventions for managing this risk. A critical evaluation and synthesis of on-going holistic care needs based on biopsychosocial model and the importance of different multi-disciplinary agencies working together are discussed. Implications for future practice concerns clinical practice, nursing education, policy and service development. These implications are crucial for understanding the comorbidity of mental health patients, developing expertise in managing mental health and physical health issues including managing chronic health conditions.</p
Precision at heart: an IoT-based vertical federated learning approach for heterogeneous data-driven cardiovascular disease risk prediction
No full textBackground: Cardiovascular disease (CVD) seriously threatens individual health, highlighting the importance of early detection and proactive mitigation. With advances in consumer electronics such as wearables and IoT, an opportunity exists to enhance CVD prediction for users. Machine learning (ML) has been widely used for predicting CVD risk (high/low) based on various factors and is a critical area of healthcare research. However, sharing data needed to predict CVD with machine learning models is challenging due to privacy concerns. Federated learning (FL) enables distributed training of ML models without sharing raw data. However, it requires that all training features be available to all clients. Methods: To address this problem, we propose a method based on vertical federated learning (VFL) designed for consumer electronics platforms. The proposed method trains the Neural Network (NN) model in a distributed manner in which different parties hold different data features. In this work, each party maintains a portion of separate data features, performs calculations on them locally, and then transfers only the necessary information to train a NN model jointly. We employ the proposed method for different use cases where the dataset features are distributed between: (i) the patient and the hospital (2-splits); (ii) the patient, the doctor, and the laboratory (3-splits); and (iii) the patient, the doctor, the Electrocardiogram (ECG) center, and the laboratory (4-splits). Results: Using a realistic dataset publicly available, we test the proposed methodology, which gives around 90% accuracy, precision, recall, and f-score. It also does not need clients to possess the same features as compared to traditional Federated Learning.Conclusion: This paper has an impact on the healthcare sector, where user data privacy is of utmost concern. This advancement will improve the ability of the healthcare sector to diagnose various diseases and contribute to the field of AI by improving distributed AI algorithms.</p
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