Abstract
Nowadays, health systems comprise a series of resources structured to provide healthcare services to meet our health needs. However, premature deaths still occur. To quantify and understand personal healthcare conditions affecting such amenable mortality, the Healthcare Access and Quality Index (HAQI) was put forward, evaluating 195 countries and territories since 1990. Nevertheless, the literature acknowledges a series of limitations of this framework, such as the drawbacks of using principal component analysis to aggregate individual indicators, the absence of control for financing and environmental conditions, and the presence of a substantial degree of data uncertainty. Accordingly, we propose a methodological alternative to the computation of the HAQI using a novel fuzzy Data Envelopment Analysis model to handle the aforementioned shortcomings. We also propose its extension towards the quantification of efficiency (E-HAQI) - in the sense of value for money - by incorporating financial aspects as modelling inputs. This way, we contribute with innovative modelling approaches that can also deal with the high degree of data uncertainty. Furthermore, in a second -stage analysis, the impact of key exogenous factors on healthcare access and quality is assessed via non -parametric hypothesis testing. Our results show positive and significant correlations of both the revisited HAQI and E-HAQI with the original HAQI 2016 dataset. They also reveal a better use of resources by European and Oceanian countries and territories than by Sub-Saharan African ones. Concerning contextual determinants, socio-demographic development, human development, and the type of health system were found to be statistically significant drivers of healthcare access and quality efficiency.

Abstract
The complex trajectories of higher education students are deviations from the regular path due to delays in completing a degree, dropping out, taking breaks, or changing programmes. In this study, we investigated degree changing as a cause of complex student trajectories. We characterised cohorts of students who graduated with a complex trajectory and identified the characteristics that influenced the time to graduation. To support this predictive task, we employed machine learning techniques such as neural networks, support vector machines, and random forests. In addition, we used interpretable techniques such as decision trees to derive managerial insights that could prove useful to decision-makers. We validated the proposed methodology taking the University of Porto (Portugal) as case study. The results show that the time to degree (TTD) of students with and without complex trajectories was different. Moreover, the proposed models effectively predicted TTD, outperforming two benchmark models. The random forest model proved to be the best predictor. Finally, this study shows that the factors that best predict TTD are the median TTD and the admission regime of the programme of destination of transfer students, followed by the admission average of the previous programme. By identifying students who take longer to complete their studies, targeted interventions such as counselling and tutoring can be promoted, potentially improving completion rates and educational outcomes without having to use as many resources.

Abstract
Diabetic foot is a complication that carries a considerable risk in diabetic patients. The consequent loss of protective sensitivity in the lower limbs requires an early diagnosis due to the imminent possibility of ulceration or amputation of the affected limb. To assess the loss of protective sensitivity, the 10 gf Semmes-Weinstein (SW) monofilament is the most used first-line procedure. However, the used device is most often non-calibrated and its feedback can lead to decision errors. In this paper we present an equipment that is able to automatically conduct a metrological verification and evaluation of the 10 gf SW monofilament in the assessment of the loss of protective sensitivity. Additionally, the proposed equipment is able to simulate the practicioner’s procedure, or can be used for training purposes, providing force-feedback information. After calibration, displacement vs. buckling force contours were plotted for three distinct monofilaments, confirming then ability of the equipment to provide fast, detailed and precise information.

Abstract
Luxembourg, known for its immigration history, attracts immigrants to work. This study analyses different immigrant groups in the labour market from 2014 to 2022 by using Labor Force Survey (LFS) data, Symbolic Data Analysis (SDA), and the Monitoring the Evolution of Clusters (MEC) framework. Based on the birthplace and length of residence in Luxembourg, in each year, microdata were aggregated into 21 symbolic objects. They were primarily described by 16 modal variables which are multi-valued variables with a frequency attached to each category. Moreover, clustering using complete linkage and the Chernoff’s distance was applied. The Heuristic Identification of Noisy Variables (HINoV) suggested that with just six variables, objects may be grouped homogeneously. The MEC framework traced temporal relations and transitions between the clusters, revealing some movements across the different years. Results indicate that people from the European Union (EU) and Neighbouring countries have similar profiles while the Portuguese have opposite characteristics. The Luxembourgers are somewhere in between. Profiling people from non-EU countries was challenging. The data and methodology used make it easy to replicate the work in other nations, enabling comparison of results and monitoring to continue in the future.

Abstract

Abstract
Purpose - The purpose of this paper is to explore the challenges, interplay and potential directions for future service research to address the first three Sustainable Development Goals (SDGs) of no poverty, zero hunger and good health and well-being. Design/methodology/approach - This commentary examines how service research has addressed these SDGs in the literature, and through the development of a theory of change, the authors propose an agenda for service research going beyond serving, to enabling and transforming service systems, expanding the current focus on individual to community and population well-being through promotion and prevention.Findings - Service research has increasingly advocated human-centered approaches but requires a shift towards an all of humanity perspective. Individual and collective well-being have gained attention in service research, emphasizing the importance of considering collective well-being.Research limitations/implications - The commentary underscores the need for a comprehensive approach to develop services that contribute to the well-being of the human species. It calls for research that transcends dyadic interactions, considers systemic dynamics and broadens the focus from individual to collective and population well-being. Social implications - This paper discusses important societal issues of poverty, hunger and good health and well-being and the need for integrated and ecosystem approaches to develop equitable and sustainable solutions for collective well-being.Originality/value - While SDGs 1, 2 and 3 address individual goals, they collectively underpin the well-being of communities and societies.