The worldwide acceleration of digital transformation in healthcare underscores the need for professionals to continuously adapt and sustain robust digital health competence, shaped not only by individual characteristics and institutional environments but also by broader social, cultural, and geopolitical factors. This study aimed to identify distinct clusters of digital health competence among healthcare professionals across 19 diverse countries and regions, and to examine the factors influencing the development and distribution of these competence clusters. A cross-sectional international survey study was conducted between 2023 and 2024, using a validated survey instrument measuring digital health competence and its influencing factors. Data were collected from healthcare professionals in 19 countries and regions (n=6440; n=5945 used for this study), following a harmonised protocol with shared demographic templates and instruments. K-means cluster analysis was employed to derive digital competence profiles, with comparative analyses conducted to investigate associations between the identified clusters and individual characteristics (e.g., age, education, professional experience). Five distinct clusters of digital health competence were identified: (1) Beginners, (2) Developing Professionals, (3) Emerging Users, (4) Proficient Practitioners, and (5) Pioneers. Higher competence clusters (4 and 5) were associated with younger age, higher education, hospital-based work, and stronger perceived support from management, organisational structures, and colleagues. In contrast, lower-performing clusters reported limited digital engagement and minimal support. Perceived leadership influence, particularly managerial commitment to digital change, was a key differentiator across clusters. The findings demonstrate substantial variation in digital health competence across healthcare professionals internationally. Cluster-specific strategies, such as targeted upskilling, peer mentoring, and leadership engagement, are needed to address competence gaps. The results provide a foundation for policy development and workforce training frameworks aimed at strengthening digital readiness in global healthcare systems. Future research should explore longitudinal competence development and evaluate targeted interventions.

Bias in hair cortisol measures for psychological stress: Self vs. professional collection.

Greater consumption of food prepared out of the home (OOH) is associated with higher energy intake. Strategies are needed to make eating OOH food less harmful to health. Identifying menu characteristics associated with higher energy consumption could aid characterisation of OOH outlets by their relative healthiness and inform future policy intervention in the OOH food sector. This study aimed to identify whether outlet healthiness rating tools and food menu characteristics can explain variance in energy consumed during OOH food eating occasions. Customers (N=3718) were asked to recall their food orders upon exiting a range of OOH outlets across four local authorities in England during 2021 and 2022. For each outlet, universal health rating scores were calculated based on select menu characteristics and deep learning healthiness scores were calculated based on outlet name. Random forest models and robust linear regression models clustered by outlet were used to identify whether outlet healthiness scores and individual menu characteristics were associated with kcal consumed. Energy consumed during OOH outlet visits was negatively associated with universal health rating scores (-28.3; 95% CI -44.8 to -11.8; p=.003) but not associated with deep learning scores. Menu characteristics with the greatest importance and therefore contributing the most to predictive accuracy for energy consumed were the percent of savoury main menu items over 600kcal and 1345kcal, the number of desserts, the number of unique vegetables, and the percent of drinks over 100kcal. Menu characteristics accounted for 29% of variance in energy consumed by customers. Universal health rating scores may be a useful tool to characterise the healthiness of OOH outlets in England. Investigating the potential impact of OOH outlet health ratings on consumer and business behaviour is warranted.

There is growing interest in tailoring treatment decisions to individual patient characteristics, but few studies have examined the implementation and performance of individualized treatment rules (ITRs) for count data. Our objective was to compare ITR methods in randomized trials with count outcomes and explore the impact of sample size and distribution of heterogeneity of treatment effect (HTE) on the validity of treatment recommendations. We conducted a simulation study where patients were randomized to receive one of two treatments and created five responder strata to reflect different HTE scenarios. Various ITR methods were used to estimate treatment effects and were evaluated in terms of value function and accuracy. We also conducted a case study involving patients with multiple sclerosis. All ITR methods performed better under favorable conditions such as larger sample size, greater treatment heterogeneity, or fewer neutral patients (also known as equivalent treatments effects), but they were outperformed by fixed treatment strategies with smaller sample sizes or limited HTE. However, larger sample sizes can compensate ITRs for smaller HTEs and high HTEs can compensate ITRs for limited data. In the case study, we identified HTE and developed a tree-based ITR that outperformed fixed treatment recommendations. In conclusion, ITR performance can be influenced by sample size and the distribution of HTE, as well as their interactions. Simulation scenarios, informed by clinical insights, can help us determine if HTE estimation is feasible and, if so, identify the most effective ITR.

Summary of best evidence for nutritional and dietary interventions in managing chemotherapy-induced gastrointestinal toxicity in cancer patients.

Unsupervised SAM-guided mixture-of-multimodal-experts fusion network for medical image diagnosis.

The 988 Suicide and Crisis Lifeline was launched in July 2022 as an alternative to 911 for mental health crises. However, little is known about the factors influencing decisions to use 911 or 988 among individuals most likely to benefit from 988, including those with suicidal ideation. This study analyzes individual characteristics associated with the likelihood of calling 911 during a mental health crisis and examines awareness of, willingness to use, and concerns about 988. An anonymous online survey was conducted in early 2024 with 1,894 U.S. adults aged 18-50, oversampling racial minorities. Logistic regression models were estimated for four dichotomized outcomes: likelihood of calling 911 in a mental health crisis, awareness of 988, willingness to use 988, and concerns about 988. Independent variables included demographic characteristics, mental health indicators, perceived stigma, and financial stress. Overall, 53.9% of respondents reported they would call 911 in a mental health crisis, and 22.2% of respondents had heard of 988. After receiving basic information, 71.5% expressed willingness to use 988. However, 87.1% reported at least one concern, and individuals with suicidal ideation were much less likely to express willingness to use 988. Commonly endorsed concerns included law enforcement involvement, forced hospitalization, and service fees. Perceived stigma and financial stress were strongly associated with a higher likelihood of expressing concern. Although many individuals expressed willingness to use 988 when informed, widespread concerns may limit uptake. Our findings suggest that targeted outreach and accurate information could promote greater utilization.

The purpose of the study is to explore the potential of novel genetic markers in predicting and diagnosing hypertensive conditions during pregnancy, focusing on their role in key pathological processes. The relevance of this study is determined by the necessity to improve methods for the early detection and effective management of pregnancy complications, particularly those associated with elevated blood pressure, which pose a threat to the health of both mother and foetus. The research methodology involves a systematic analysis of scientific literature from the past five years, specifically from 2019 to 2024, emphasising the integration of data from various populations and research approaches within the field of molecular medicine. The main findings demonstrate that certain molecular markers reflecting gene activity exhibit high accuracy in predicting the risks of complications associated with hypertension of pregnancy. It is established that these markers can detect disturbances in the regulation of vascular tone, angiogenesis processes, and inflammatory responses long before the onset of clinical symptoms. This opens up opportunities for timely medical intervention and a personalised approach to pregnancy management. The results of the study confirm the potential of utilising genetic markers in clinical practice to improve pregnancy outcomes and reduce risks for both mother and child. The practical value lies in the possibility of developing new screening programmes and therapeutic strategies based on the individual characteristics of patients, contributing to more effective management of hypertensive disorders during pregnancy.

BACKGROUND Voice is individual and therefore several studies consider voice as a biomarker of health. Focusing on the change of voice parameters of depressed patients can provide an additional, objective measurement of the patient’s individual progress during psychotherapy. This retrospective study is focused on the analysis of voice parameters, specifically prosodic parameters, gathered from sessions and real-world data out of the Munich psychotherapy study (MPS). AIM To investigate the prosodic parameters of patients with depression, their change over a long-term therapy and correlate them with Beck depression inventory (BDI) data. The analysis on the prosodic parameters aims to find, if and to what extent, a change in the patient’s voice can be discovered and identified. This data is then correlated with already available BDI data of each individual patient. The hypothesis is that voice expression is impacted by depression and changes with the state of depression. METHODS We performed a retrospective investigation of n = 25 patients, who originally participated in the MPS, and their change in prosodic parameters. An analysis of the mean fundamental frequency fo and its standard deviation was performed on six psychotherapy sessions per patient, divided into three sessions at the beginning of the therapy and three sessions at the end of the therapy. The technical analyses on the voice part have been conducted with Praat, a phonetic analysis software developed at the University of Amsterdam. RESULTS The mean fundamental frequency was fo ,mean = 183.1 Hz at the beginning and fo ,mean = 187.8 Hz at the end, in a compounded group of all patients (men and women). The average BDI of all patients at the beginning was BDImean = 29.96 and BDImean = 9.6 at the end. A positive influence of psychotherapy can be seen in the majority of patients not only in a reduced BDI rating, but also in an increased baseline frequency. CONCLUSION With regard to the clinical picture of depression, its recognition and constant monitoring during therapy, it is important to exhaust all possibilities and examine additional relevant data. Prosodic characteristics can make a contribution here for various reasons. On the one hand, a person acts unconsciously via and through their voice and emotional states become detectable. On the other hand, voice can be represented in objective parameters and is therefore suitable for analyzing individual characteristics and changes. Dealing with the topic from the perspective of psychotherapy therefore seems unavoidable, as an additional component is included in the holistic view of the person.

Personalized Medicine (PM) also called as precision medicine, Personalized medicine has transformed cancer diagnosis and treatment by tailoring therapeutic strategies to individual genetic, molecular and phenotypic characteristics. Personalized Medicine revolutioned oncology management in high human development, Oncologist have been able to target on individual’s cancer, Pharmacogenomics, the study of how genetic variations influence drug response, plays a central role in optimizing anticancer therapy by improving efficacy, reducing toxicity, and minimizing trial-and-error prescribing. Advances in genomic technologies and bioinformatics have enabled precise tumor profiling, identification of actionable mutations, and development of targeted therapies. This review discusses the principles of pharmacogenomics in oncology, its role in cancer diagnosis, treatment selection, dose optimization, and future prospects in personalized cancer care.

The diagnosis of autism spectrum disorder (ASD), which is more common in modern times, is observed with the fact that teachers and specialists who teach these individuals face numerous difficulties in creating a quality learning environment. The main goal of educational services provided to individuals with ASD is to pay attention to their individual characteristics, needs and abilities, to equip them with the knowledge, skills and behaviors that will help them to fully participate in social life and to increase their independence, as well as their quality of life. One of the necessary conditions for achieving this goal is to provide individuals with ASD with the same educational opportunities as other individuals. Ensuring that individuals with ASD can benefit from appropriate educational opportunities throughout their lives will significantly contribute to their learning, behavioral, social and emotional development, and will help the individual live as part of society. This, in turn, can lead to an increase in the quality of life of the individual and his family in the next stage. When considering educational opportunities for individuals with ASD, features such as the development of specifically structured learning programs, the design of learning environments that take into account the learning characteristics of these individuals, and the delivery and evaluation of the program using experiments with a strong scientific basis are important.

A new approach is proposed for the use of the genetic markers to manage DUS trials, targeted at individual phenotypic characteristics using genomic prediction, as well for supporting Distinctness decisions. High-performing crop varieties underpin food security. Due to the cost of developing varieties, systems have been established to provide breeders with legal protection for their varieties. In many countries, such protection is afforded by the International Union for the Protection of New Varieties of Plants (UPOV) system. New varieties must be phenotypically Distinct from existing varieties using a set of crop-specific characteristics, as well as Uniform and Stable (DUS). For many crops, DUS assessment is costly as candidates must be compared to many existing varieties in field trials, based on numerous DUS characteristics. The use of genetic markers has long been considered as a potential tool for managing costs of such trials, for example, by identifying existing varieties that need not be compared to candidate varieties. Under UPOV guidance, the use of genetic markers must be reflective of phenotypic differences in DUS characteristics. Within this framework, we propose a new approach for using markers based on the application of genomic prediction, which is used to predict variety differences in individual characteristics. The approach is evaluated with perennial ryegrass and wheat, yielding promising results. Additionally, we propose a novel approach in which genomic prediction is used to refine Distinctness decisions after DUS trials have been run by integrating genetic and trial information. Using perennial ryegrass as an example, we demonstrate that this approach, which respects the primacy of phenotype in DUS testing, could be used to support distinctness decisions, especially for cross-pollinated agricultural crops where Distinctness may be harder to achieve.

Flow is an affective and cognitive psychological state characterized by complete absorption, intrinsic enjoyment and enhanced performance. Although flow experience in educational contexts-often termed learning flow-has attracted considerable research attention, empirical findings remain fragmented across studies and theoretical perspectives. This meta-analysis aimed to synthesize existing empirical evidence and systematically examine the antecedents and outcomes of learning flow in academic contexts. Drawing on flow theory and integrating motivational and affective frameworks, the study sought to clarify the nomological network surrounding learning flow. A meta-analysis was conducted based on 108 independent studies comprising a total sample of 42,952 participants. Effect sizes were synthesized to examine associations between learning flow and four conceptual domains: (a) learning task and learning environment characteristics, (b) individual characteristics, (c) individual attitudes and behaviours and (d) interpersonal exchange characteristics. Moderator analyses were performed to assess the potential influence of cultural context and flow measurement approaches. The results indicated that learning flow was positively associated with optimal learning tasks, supportive learning environments, adaptive learner characteristics and high-quality interpersonal interactions. Furthermore, learning flow demonstrated positive associations with key educational outcomes, including academic performance, learning attitudes and psychological well-being. Moderator analyses showed that neither cultural context nor measurement approaches significantly influenced the observed effect sizes. The findings suggest that learning flow emerges from the dynamic interplay of individual, interpersonal and contextual factors within educational environments. Given that most primary studies relied on correlational designs, the results delineate a comprehensive nomological network rather than establishing causal relationships. Overall, the meta-analysis highlights the importance of adopting multilevel perspectives when studying learning flow in educational settings. Future research should further investigate causal mechanisms and longitudinal dynamics to better understand how learning flow develops and influences academic outcomes.

The purpose of the current study was to identify the influence of individual characteristics on differences in body fat percentage between air displacement plethysmography and dual-energy X-ray absorptiometry (change in body fat percentage) in 360 collegiate student-athletes. Body fat percentage was obtained from air displacement plethysmography and dual-energy X-ray absorptiometry. A 2 (sex)×3 (Brozek body fat percentage, Siri body fat percentage, and dual-energy x-ray absorptiometry body fat percentage) analysis of variance was used to evaluate the difference between the body fat percentages obtained from air displacement plethysmography and dual-energy x-ray absorptiometry in men and women. The agreement between air displacement plethysmography and dual-energy X-ray absorptiometry was evaluated via Bland-Altman plot and linear regression. Multiple linear regression was used to identify the influence of sex, race, height, and body mass on the change in body fat percentages. Dual-energy X-ray absorptiometry yielded greater body fat percentages in both men (4.8±2.8; p < 0.001) and women (2.2±2.9; p < 0.001) compared to air displacement plethysmography measurements. Proportional and systematic biases were observed between air displacement plethysmography and dual-energy X-ray absorptiometry. Race, height, and mass were significant predictors of the body fat percentage difference between air displacement plethysmography and dual-energy X-ray absorptiometry (R 2=0.445, p<0.001, and standard error of the estimate=2.527). The present study suggests that air displacement plethysmography underestimates body fat percentages when compared to dual-energy X-ray absorptiometry and this underestimation may not be the same for every athlete due to their individual characteristics.

Objectives. This research aims to provide information on factors that may affect the accident susceptibility of workers by using various individual characteristics of workers as variables with machine learning algorithms, and the effects of occupational health and safety (OHS) training and workers’ safety awareness within this interaction. Methods. Research data were obtained through surveys administered to port workers (dockers). The data were modeled using binary logistic regression (BLR) and support vector machine (SVM) algorithms, and the results were examined comparatively. Results. Worker’s profession, education level, age, safety awareness and work experience, and the number of OHS professionals are some individual characteristics and managerial factors that significantly influence whether an accident occurs or not. Employees’ OHS examination scores do not have a significant impact on the likelihood of an accident for the worker. As workers believe their safety awareness is increasing, the likelihood of accidents also rises. Conclusion. Workers may have a misleading experience regarding training activities in ports and the managers’ approach. They may have an unrealistic sense of self-confidence. Many accidents occurring in ports are preventable. While BLR provides a greater number of more easily interpretable outputs related to accident causality, SVM offers fewer but more reliable outputs.

Purpose Entrepreneurial resilience is a complex yet highly topical phenomenon that includes individual, organizational and ecosystem elements. As an extension of this research focusing mainly on the individual characteristics and capabilities of a refugee entrepreneur (RE), this study aims to take a holistic approach to exploring how the resilience of REs is emerging in the Arctic region. Design/methodology/approach The analysis is grounded on 20 semi-structured in-depth interviews with various stakeholders in the Arctic region in Northern Finland. Findings This study’s findings underscore that resilience is integral to REs’ success. It needs to be understood as a multidimensional and dynamic phenomenon that includes individual, community and institutional levels, each with critical influential factors that show how resilience emerges in the challenging context of the Arctic region. Originality/value The findings enhance the understanding of the significance of resilience for the refugees to be able to integrate into their new hosting environment. Thus, this study not only helps to address a gap in the literature surrounding refugee entrepreneurship in the Arctic region but also a gap within the wider literature in terms of migrant entrepreneurship.

This study aims to examine how auditor competence, supervisory independence, and internal control systems influence internal audit quality and fraud prevention in Indonesia's public sector, with particular focus on the moderating role of auditor integrity in shaping the effectiveness of these oversight mechanisms. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), this study analyzes data from 53 auditors at the Buleleng Regency Inspectorate to test six direct relationships and six moderating effects of auditor integrity on internal audit quality and fraud prevention. The study reveals three key findings. First, all direct effects prove significant: internal control systems emerge as the strongest predictor of internal audit quality (β=0.431, p&lt;0.001), while auditor competence most powerfully influences fraud prevention (β=0.408, p=0.001). Second, auditor integrity exhibits complex and counterintuitive moderation patterns: it negatively moderates the competence-audit quality relationship (β=-0.264, p=0.034) and independence-audit quality relationship (β=-0.095, p=0.029), indicating compensatory mechanisms or contextual pressures in corrupt environments. Third, integrity does not moderate any relationships with fraud prevention nor internal control system relationships with either outcome, revealing that fraud prevention is more heavily determined by structural factors than individual ethical characteristics.

Discretisation is a processing step often included in the preliminary data preparation. Typically, when the input features have continuous domains and their discrete forms are needed, all are translated into categorical type at the same time, before data mining takes place. However, proceeding this way is not always the most advantageous to performance. The paper presents results from the research where the discretisation transformations were carried out sequentially forward for variables, and their selection was based on their values and also importance of the attributes estimated by the constructed rankings. The experiments were executed on the datasets from the area of stylometric analysis of texts, the application domain focused on recognising authorship based on individual characteristics of writing styles. For the selected data mining techniques, the performance was studied in the context of transformed features. The observed trends indicate that along with enhanced understanding of the nature of the data, partial discretisation of feature sets could bring higher accuracy than transformation of entire input domain, showing the merits of the described research methodology.

ABSTRACT Purpose Based on the Inputs-Environment-Outputs model, this study investigates the quality of agricultural master’s education and its influencing factors in China. Methodology Data were collected through questionnaires distributed across 11 Chinese universities. Descriptive statistics were complemented with regression models and mediating effect models to identify key determinants. Findings First, while the overall quality of agricultural master’s education proves satisfactory, there remains room for improvement. Second, gender (as an individual characteristic) and environmental factors including supervisor guidance, equipment support, course teaching, practical training, administrative services, and ideological-political education significantly enhance educational quality. Third, study engagement, practice participation, and professional identity demonstrate individual mediating effects between training environment and training quality. Moreover, study engagement and practice participation further exhibit chain mediating effects through professional identity. Practical Implications This study provides insights for reforming agricultural master’s training models and informs educational administrators. Theoretical Implications The study clarifies relationships between individual characteristics, training environment, and training quality, particularly through mediating effect exploration, thereby enriching theoretical frameworks in agricultural graduate education. Originality/Value Establishing a systematic analytical framework, this study offers perspectives for both educational practitioners and scholars in agricultural master’s education.

This study investigates the diversity of attitudes toward climate change risk (ACCR) among older adults in England. This demographic, both vulnerable to climate impacts and influential in shaping climate policy, has often been overlooked in terms of its specific ACCR. The study aims to identify distinct attitudinal profiles and explore the sociodemographic, economic, health, and civic factors associated with them. Using data from Wave 11 (2023-2024) of the nationally representative English Longitudinal Study of Ageing, we analysed responses from 6,572 individuals aged 50 and older. Latent class analysis was employed to identify typologies of ACCR based on six climate-related statements. Multinomial logistic regression was used to examine associations between class membership and individual characteristics. Five distinct ACCR profiles were identified: "Highly engaged with climate change risk" (30.3%), "Engaged with CCR" (31.3%), "Risk-aware but fatalistic" (11.1%), "Ambivalent/uncertain about CCR" (21.5%), and "CCR dismissive" (5.8%). Younger age, higher education, greater financial resources, and higher levels of public engagement were associated with a higher likelihood of being "Highly engaged" about climate change. Conversely, lower education, economic hardship, and lack of civic engagement were linked to "ambivalent/uncertain" attitudes. Notably, older adults were more likely to be risk-aware but fatalistic. Contrary to common assumptions, most older adults are engaged with CCR, but there is notable heterogeneity, with ∼27% reporting ambivalent/uncertain, or dismissive views. Inclusive and effective climate policy should recognise this diversity, employing outreach and communication strategies that stress personal relevance and actionable solutions, especially targeting those with ambivalent/uncertain views.