Numerous Decisions Research Articles

Are three zebras more than three frogs: examining conceptual and physical congruency in numerosity judgements of familiar objects.

Researchers in numerical cognition have extensively studied the number sense-the innate human ability to extract numerical information from the environment quickly and effortlessly. Much of this research, however, uses abstract stimuli (e.g., dot configurations) that are also strictly controlled for their low-level visual confounds, such as size. Nonetheless, individuals rarely extract numerical information from abstract stimuli in everyday life. Yet, numerical judgments of familiar objects remain poorly understood and understudied. In the current study, we examined the cognitive mechanisms underlying the numerical decisions of familiar objects. In two experiments, we asked adult participants (Experiment 1) and two groups of children (aged 7-9years and 11-12years, Experiment 2) to perform an animal numerosity task (i.e., "Which animal is more numerous?"), while the conceptual congruency (i.e., the congruency between an object's real-life size and its numerosity) and physical congruency (the congruency between the number of items and the total space they occupy on the screen) were manipulated. Results showed that the conceptual congruency effect (i.e., better performance when the animal with a larger size in real life is more numerous) and a physical congruency effect (i.e., better performance when the physically larger animal is more numerous) were present in adults and children. However, the effects differed across the age groups and were also a subject of developmental change. To our knowledge, this study is the first one to demonstrate that conceptual knowledge can interfere with numerosity judgements in a top-down manner. This interference effect is distinct from the bottom-up interference effect, which comes from the physical properties of the set. Our results imply that the number sense is not a standalone core system for numbers but is embedded in a more extensive network where both low-level and higher-order influences are possible. We encourage numerical cognition researchers to consider employing not only abstract but also familiar objects when examining numerosity judgements across the lifespan.

Advancing loss reserving: A hybrid neural network approach for individual claim development prediction

AbstractAccurately estimating loss reserves is critical for the financial health of insurance companies and informs numerous operational decisions. We propose a novel neural network architecture that enhances the prediction of incurred loss amounts for reported but not settled claims. Moreover, differing from other studies, we test our model on proprietary datasets from a large industrial insurer. In addition, we use bootstrapping to evaluate the stability and reliability of the predictions and Shapley additive explanation values to provide transparency and explainability by quantifying the contribution of each feature to the predictions. Our model shows superiority in estimating reserves more accurately than benchmark models, like the chain ladder approach. Particularly, our model exhibits nuanced performance at the branch level, reflecting its capacity to effectively integrate individual claim characteristics. Our findings emphasize the potential of using machine learning in enhancing actuarial forecasting and suggest a shift towards more granular data applications.

Optimizing Well Placement with Genetic Algorithms: A Case Study

Maximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a type of stochastic optimization technique that has proven effective in solving various problems. The results of the study show significant improvements in NPV when using genetic algorithms compared to traditional methods, particularly for problems with numerous decision variables. The findings suggest that genetic algorithms are a promising tool for optimizing well placement in oil field development, improving NPV, and reducing the risk of project failure.

The Impact of Health Education on Hemoglobin A1C in Diabetic Patients at the Family Medicine Department of King Fahad Armed Forces Hospital in Jeddah.

Diabetes mellitus (DM) is a long-term condition associated with severe complications. Individuals with diabetes must make numerous self-management decisions and participate in diverse care activities. Diabetes self-management education and support assist patients in making these decisions and performing these activities, enhancing their health outcomes. The study aims to assess the effects of health education on median hemoglobin A1c (HbA1C) levels, the development of diabetic complications, and the number of hospital admissions in patients with uncontrolled type 2 DM. This prospective quasi-experimental study, conducted at King Fahad Armed Forces Hospital in Jeddah, Saudi Arabia, from September 2020 to September 2022, assessed the impact of a structured diabetic education program on uncontrolled type 2 DM patients. The study involved 100 patients with HbA1c >8%: 50 in the intervention group who received the program and 50 in the control group who did not. HbA1c levels were measured before and after the intervention. Data was collected securely, and an experienced biostatistician performed statistical analysis. The two groups found no significant differences in age, disease duration, HbA1c, medication type, insulin type, education level, employment, and clinical visits. However, the control group had significantly more females (40 (81.6%) vs. 32 (64%), p=0.049), and the intervention group had larger family sizes (43 (86%) with >4 members vs. 21 (42.9%), p<0.0001). The intervention group showed a significant decrease in HbA1c from baseline across all measurements post-educational program (p<0.0001), whereas the control group did not show significant changes. Economic status also differed significantly (p=0.024). No significant differences were found between groups in follow-up HbA1c measurements. The study demonstrates that the diabetic education program at King Fahad Armed Forces Hospital effectively lowered HbA1c levels in type 2 DM patients, confirming the program's role in enhancing glycemic control through structured self-management education and support.

Sentinel-1A Data Analysis for Rice Classification Utilizing Random Forests and Support Vector Machine

Rice is known to be one of the most important crops in India and many other nations, particularly in Asia, therefore accurate rice area estimation has an important role in many activities, ranging from human nutrition to environmental concerns. As a result, the determination of cultivation area remains a hot topic among researchers from numerous disciplines, planners, and decision makers. Using Sentinel-1A SAR (Synthetic Aperture Radar) satellite data, this study attempts to evaluate the effectiveness of random forest (RF) and support vector machines (SVM) algorithms for rice crop classification. According to the findings, rice fields can easily be distinguished from other crops in the research area by using the temporal characteristics of the rice crop as reflected in the VH backscatter patterns. The total precision and Kappa coefficient produced by RF showed 85.7% and 0.74, respectively, when the classification outcomes were compared to the ground reference data. These values were somewhat higher than those obtained by SVM (81.2% overall accuracy and 0.68 Kappa coefficient). The government’s rice area statistics were used to compare the analysis results; the relative difference in rice area for RF and SVM, respectively, came out to be +1.40% and -4.63%. In summary, the RF algorithm is highly recommended for the accurate differentiation of rice fields from neighbouring classes in conditions of identical climate, soil, and topography with similar methods of cultivation. On the other hand, Sentinel-1A SAR provides a valuable data set at cost-free for similar studies.

Adaptation of the Diet Quality Questionnaire as a Global Public Good for Use in 140 Countries

BackgroundFood group consumption data are useful for measuring and monitoring diet quality. To collect valid data across contexts, consistent and rigorous adaptation of survey questions is needed. ObjectivesThe objective of this research was to adapt food group consumption survey questions for 140 countries, by identifying the most common (sentinel) food items in each food group using a structured, participatory process and global standards for classification. MethodsSurvey questions were adapted for 29 food groups of the diet quality questionnaire (DQQ) and for additional questions for infant and young child feeding (IYCF) indicators. For each country, adaptation comprised the following: 1) review of existing questionnaires, dietary intake data, and other information to draft food lists; 2) key informant (KI) interviews with 5–12 experts to identify and prioritize sentinel items including terminology; 3) comparison of items across countries within the same region to identify discrepancies, and follow-up with KIs to resolve them. ResultsIn total, 1016 KIs contributed to the adapted DQQs for 140 countries and IYCF DQQs for 96 countries, amounting to ∼9550 h of collective effort (68 person-hours/country on average) from 2020 to 2024. The process revealed numerous challenges and decisions to ensure consistent classification of items and valid question formulation. ConclusionsCountry-specific questions adhering to global standards, and adapted through cumulative and iterative input of local experts, enable the collection of food group consumption data that are valid and comparable across time and geographies. The adapted survey questions have been implemented in the Demographic and Health Surveys and Gallup World Poll in 94 countries, generating the first cross-country data on minimum dietary diversity and other diet quality indicators. The finalized country-adapted DQQs and IYCF DQQs were translated to 143 national languages and are published online as a global public good for population-level diet quality measurement.

Optimal management in island microgrids using D-FACTS devices with large-scale two-population algorithm

Amidst the increasing complexity of microgrid optimization, characterized by numerous decision variables and intricate non-linear relationships, there is a pressing need for highly efficient algorithms. This study introduces a tailored Mixed Integer Nonlinear Programming (MINLP) model that optimizes the charging and discharging schedules of electric vehicles (EVs) and energy storage systems (ESS) while incorporating Distributed Flexible AC Transmission System (D-FACTS) devices. To address these challenges, a novel approach based on the Large-Scale Two-Population Algorithm (LSTPA) is proposed. The model's effectiveness was evaluated using a 33-node microgrid, where the proposed method achieved a total purchased energy of 1.2 MWh, a voltage deviation of 0.0357 p.u, and a CPU time of 551 s, outperforming traditional methods like NSGA-II, PSO, and JAYA. Additionally, in a 69-node microgrid, the approach resulted in a total purchased energy of 0.3 MWh and a voltage deviation of 0.0078 p.u. These results demonstrate the superior performance of the proposed method in terms of energy efficiency, voltage stability, and computational time, advancing the efficiency of microgrid management.

Learning-Based Optimisation for Integrated Problems in Intermodal Freight Transport: Preliminaries, Strategies, and State of the Art

Intermodal freight transport (IFT) requires a large number of optimisation measures to ensure its attractiveness. This involves numerous control decisions on different time scales, making integrated optimisation with traditional methods almost unfeasible. Recently, a new trend in optimisation science has emerged: the application of Deep Learning (DL) to combinatorial problems. Neural combinatorial optimisation (NCO) enables real-time decision-making under uncertainties by considering rich context information—a crucial factor for seamless synchronisation, optimisation, and, consequently, for the competitiveness of IFT. The objective of this study is twofold. First, we systematically analyse and identify the key actors, operations, and optimisation problems in IFT and categorise them into six major classes. Second, we collect and structure the key methodological components of the NCO framework, including DL models, training algorithms, design strategies, and review the current State of the Art with a focus on NCO and hybrid DL models. Through this synthesis, we integrate the latest research efforts from three closely related fields: optimisation, transport planning, and NCO. Finally, we critically discuss and outline methodological design patterns and derive potential opportunities and obstacles for learning-based frameworks for integrated optimisation problems. Together, these efforts aim to enable a better integration of advanced DL techniques into transport logistics. We hope that this will help researchers and practitioners in related fields to expand their intuition and foster the development of intelligent decision-making systems and algorithms for tomorrow’s transport systems.

The problem of proving the use and non-use of a trademark arises in the context of disputes on revocation of a Ukrainian trademark certificate

The article contains a practical analysis of the challenges faced by parties in Ukrainiancourt when proving the use or non-use of a trademark in a dispute on the revocation (earlytermination) of the Ukrainian trademark certificate. From the theoretical and practical pointof view, the problem arises due to the absence of a direct legal provision and well-establishedcourt practice regarding the sufficient scope and manners of a trademark use, when each ofthe parties must prove the opposite facts before the court — use and non-use of the trademarkin question. Thus, the essence of such litigation revolves around the plaintiff’s need to provethe non-use and the defendant’s need to prove the use of the trademark in question. In general,the following questions arise: Is the trademark certificate owner (the defendant) obliged touse the trademark to label goods (proving its real, genuine use) for each good under the Niceclassification? Additionally, how many of the manners of using the trademark, as listed inlaw, are sufficient to confirm real (genuine) use? These questions arise in every court case,since this is not directly regulated in the laws of Ukraine. Consequently, the parties oftenrefer to Article 198 of the Association Agreement between the EU and Ukraine regarding«genuine use» as opposed to «formal/symbolic use» of the trademark.In a result of conducted research and made analysis, it appears that the subject of proof insuch cases is the defendant’s (or licensee’s) use (or non-use) of the trademark for eachgood/service listed in the trademark certificate, and the use must be demonstrated in at leastone of the manners listed in Art. 16(4) of the Law of Ukraine «On Protection of Rights toMarks for Goods and Services» dated 15.12.1993 No.3689-XII. Therefore, a proper and admissibleevidence of trademark use may include presenting the court with the labeled good, aswell as any documents, writings, papers, which demonstrate use of a trademark in, at least,one manner per each good/service for which a trademark is registered. Article contains withextracts from the numerous court decisions to provide for examples of written documents,submitted by parties in alike cases, as evidence of trademark use (non-use). Theoretical conclusionsand practical recommendations are summarized based on analysed court decisions.

Large-scale multiple criteria group decision-making with information emendation based on unsupervised opinion evolutions

Large-scale multiple criteria group decision-making (MCGDM) is prevalent in diverse decision-making scenarios, involving numerous decision makers (DMs), the set of alternatives and criteria, and continuous temporal cycles. Opinions from DMs dynamically evolve through iterative interaction, leading to dynamic opinion evolutions. However, traditional MCGDM methodology usually establish the opinion formation on a static time point throughout information aggregation, which will lead to information distortion. This study develops a novel large-scale MCGDM method with information emendation based on an unsupervised opinion dynamics (UOD) model, combining with the intuitionistic fuzzy set (IFS) and the technique for order preference by similarity to an ideal solution (TOPSIS). The IFS is utilized to quantify opinions since it can effectively achieve a tradeoff between information retention and convenience of evaluation. Simultaneously, in the proposed UOD model, the weight updating mechanism is further considered to improve the interaction adequacy, and the unsupervised mechanism for interaction threshold helps to decrease the influences of subjectivity from DMs. Moreover, numerical simulations validate the UOD model’s feasibility. Finally, a school site selection problem is carried out to elaborate the effectiveness of the proposed method. This study will provide a methodological reference for solving large-scale MCGDM problems, facilitating rapid convergence of opinions within large-scale groups, and enrich the research on opinion dynamics in the field of decision-making.

No such thing as waste in primary food sector.

The European Union (EU) circular economy action plan aims to double its use of recycled material by 2030. We argue that waste-centric approach to resources may have adverse consequences to this ambition. The aim of the work was to find out the factors limiting or promoting the use of waste from primary food sector in countries with the same cultural background and similar climate in Estonia, Latvia, Lithuania and Norway. Biomass from the primary food production sector is of good quality and excellent to use, but its use may be limited if given waste status. From numerous management decision trigger clusters, which may affect valorisation of the biomass, we focus on technology and policy. Our semi-structured interviews addressed the analysis and management of waste or by-products and explored the end-of-waste and alternative mechanisms that allowed the biomass to be valorised. However, the interviews revealed that none of the companies regard anything becoming waste, but as raw material or production left-over. Any obstacles hypothesised turned out not to be acknowledged by companies at all. This appears to be a very good example of the use of resources, but the approach is haphazard and may conflict with official understanding and waste reporting requirements. Definition of waste is the same in the EU, and arbitrary treatment can be misleading. There is a need for better management of the material flow to ensure effective biomass circulation avoiding its becoming waste. We recommend that this be addressed by introducing environmental, social, governance and a self-control system.

Identifying diseases symptoms and general rules using supervised and unsupervised machine learning

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases.

Assessing Decision Fatigue in General Practitioners' Prescribing Decisions Using the Australian BEACH Data Set.

General practitioners (GPs) make numerous care decisions throughout their workdays. Extended periods of decision making can result in decision fatigue, a gradual shift toward decisions that are less cognitively effortful. This study examines whether observed patterns in GPs' prescribing decisions are consistent with the decision fatigue phenomenon. We hypothesized that the likelihood of prescribing frequently overprescribed medications (antibiotics, benzodiazepines, opioids; less effortful to prescribe) will increase and the likelihood of prescribing frequently underprescribed medications (statins, osteoporosis medications; more effortful to prescribe) will decrease over the workday. This study used nationally representative primary care data on GP-patient encounters from the Bettering the Evaluation and Care of Health program from Australia. The association between prescribing decisions and order of patient encounters over a GP's workday was assessed with generalized linear mixed models accounting for clustering and adjusting for patient, provider, and encounter characteristics. Among 262,456 encounters recorded by 2,909 GPs, the odds of prescribing antibiotics significantly increased by 8.7% with 15 additional patient encounters (odds ratio [OR] = 1.087; confidence interval [CI] = 1.059-1.116). The odds of prescribing decreased significantly with 15 additional patient encounters by 6.3% for benzodiazepines (OR = 0.937; CI = 0.893-0.983), 21.9% for statins (OR = 0.791; CI = 0.753-0.831), and 25.0% for osteoporosis medications (OR = 0.750; CI = 0.690-0.814). No significant effects were observed for opioids. All findings were replicated in confirmatory analyses except the effect of benzodiazepines. GPs were increasingly likely to prescribe antibiotics and were less likely to prescribe statins and osteoporosis medications as the workday wore on, which was consistent with decision fatigue. There was no convincing evidence of decision fatigue effects in the prescribing of opioids or benzodiazepines. These findings establish decision fatigue as a promising target for optimizing prescribing behavior. We found that as general practitioners progress through their workday, they become more likely to prescribe antibiotics that are reportedly overprescribed and less likely to prescribe statins and osteoporosis medications that are reportedly underprescribed.This change in decision making over time is consistent with the decision fatigue phenomenon. Decision fatigue occurs when we make many decisions without taking a rest break. As we make those decisions, we become gradually more likely to make decisions that are less difficult.The findings of this study show that decision fatigue is a possible target for improving guideline-compliant prescribing of pharmacologic medications.

CYSTATIN C-A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes.

Chronic kidney disease (CKD) is a microvascular complication that frequently affects numerous patients diagnosed with diabetes. For the diagnosis of CKD, the guidelines recommend the identification of the urinary albumin/creatinine ratio and the determination of serum creatinine, based on which the estimated rate of glomerular filtration (eGFR) is calculated. Serum creatinine is routinely measured in clinical practice and reported as creatinine-based estimated glomerular filtration rate (eGFRcr). It has enormous importance in numerous clinical decisions, including the detection and management of CKD, the interpretation of symptoms potentially related to this pathology and the determination of drug dosage. The equations based on cystatin C involve smaller differences between race groups compared to GFR estimates based solely on creatinine. The cystatin C-based estimated glomerular filtration rate (eGFRcys) or its combination with creatinine (eGFRcr-cys) are suggested as confirmatory tests in cases where creatinine is known to be less precise or where a more valid GFR estimate is necessary for medical decisions. Serum creatinine is influenced by numerous factors: age, gender, race, muscle mass, high-protein diet, including protein supplements, and the use of medications that decrease tubular creatinine excretion (H2 blockers, trimethoprim, fenofibrate, ritonavir, and other HIV drugs). The low levels of creatinine stemming from a vegetarian diet, limb amputation, and conditions associated with sarcopenia such as cirrhosis, malnutrition, and malignancies may lead to inaccurately lower eGFRcr values. Therefore, determining the GFR based on serum creatinine is not very precise. This review aims to identify a new perspective in monitoring renal function, considering the disadvantages of determining the GFR based exclusively on serum creatinine.

Early identification of people at high risk of oral cancer-A review of existing risk prediction models.

Prediction plays a ubiquitous role in cancer care. At every stage of the illness, the patient, the physician, and the family must make numerous decisions. Utilizing epidemiological, clinical, biological, lifestyle, and genetic factors, a cancer-specific risk assessment model calculates the likelihood of developing cancer. In India, oral cancer ranks as the fourth most common cancer, affecting nearly 3,000,00 individuals annually. Because it is in the premalignant stage, oral cancer is easily detectable in the oral cavity. Prompt identification of this lesion can result in better outcomes and a higher standard of living. Advanced statistical techniques have been used to develop prediction algorithms or risk scores that identify individuals with a high risk of developing oral cancer. With the aid of these risk assessment models, specific individuals can be screened to aid in the early detection of the disease, which may result in better outcomes and lifestyle modifications. Finding the best model among the current risk models for oral cancer may be aided by a thorough examination of all these models. Finding and assessing the risk model that primary care physicians can use and easily apply in clinical practice will be made easier with a succinct and straightforward comparison of the models. This review compares the current models to determine which has the best performance metrics, which could lead to a better understanding of the advantages and disadvantages of various risk prediction models of oral cancer.

Exploring the acceptability of a community-enhanced intervention to improve decision support partnership between patients with chronic kidney disease and their family caregivers.

Patients face numerous health-related decisions once advanced chronic kidney disease (CKD) is diagnosed. Yet, when patients are underprepared to navigate and discuss health-related decisions, they can make choices inconsistent with their expectations for the future. This pilot study, guided by the multiphase optimization strategy and community-engaged research principles, aimed to explore the acceptability of a developed patient component to a decision-support training intervention called ImPart (Improving Decisional Partnership of CKD Dyads). CKD patients and their family caregivers were recruited from an urban, academic medical center. Eligibility criteria for patients included a diagnosis of stage 3 or higher CKD (on chart review), and caregivers participated in interview sessions only. Patients without a caregiver were not eligible. The intervention was lay coach, telephone-delivered, and designed to be administered in 1-2 week intervals for 4 sessions. An interview guide, developed in collaboration with an advisory group, was designed to ascertain participants' experiences with the intervention. Caregiver interviews focused on changes in the patient's decision ability or engagement. Thirteen patients and eleven caregivers were interviewed. The program was viewed as "good" or "beneficial." Three themes capture the intervention's impact- 1) Frequent and deliberate disease-focused communication, 2) Future planning activation, and 3) Coaching relationship. The piloted intervention was successfully delivered, acceptable to use, and found to promote enhanced disease and future planning communication. By undergoing this work, we ensure that the patient component is feasible to use and meets the needs of participants before implementation in a larger factorial trial.

Technically speaking: How the Gates Foundation governs ‘women’s health’ in India

In global health, the ‘technical’ is viewed as scientifically and ethically superior due to the emphasis on quantitative methods, supported by international aid and philanthropy. This perceived objectivity and selflessness obscure numerous strategic decisions and practices through which donor-funded programmes shape the standards of women’s health. I argue that this unexamined belief in the technical approach is a key reason why ‘Reproductive, Maternal, Neonatal, and Child Health’ (RMNCH) remains dominant despite its conceptual limitations and uneven progress. An in-depth study of RMNCH projects in Uttar Pradesh funded by the Gates Foundation shows how technical interventions align with local social norms and political dynamics, creating an ideal-type female participant. In sum, this shapes the meaning and scope of women’s health.

Effects of counseling strategies among undergraduate students' achievement in measurement and evaluation in a tertiary institution in Anambra state

This quasi-experimental study investigates the effects of counseling strategies on undergraduate students’ achievement in Measurement and Evaluation within tertiary institutions in Anambra State. The research design involved a random sample of 20 undergraduate students enrolled in Measurement and Evaluation courses across various tertiary institutions. The study employed three distinct counseling strategies: Cognitive-Behavioral Therapy (CBT), Person-Centered Therapy, and Solution-Focused Brief Therapy (SFBT). The researcher designed an Achievement Test in Measurement and Evaluation (AATME) as the primary instrument for data collection, comprising of numerous decision questions lined up with the educational plan. Pre-test and post-test measures were directed to the two gatherings, and the information were investigated utilizing mean and standard deviation. The discoveries expect to give understandings into the differential impacts of directing techniques on understudies’ scholarly accomplishment in Estimation and Assessment. By looking at pre-test and post-test scores between gatherings, the review adds to the current writing on guiding adequacy in instructive settings. The examination holds suggestions for teachers, guides, and policymakers, offering proof-based understandings into the plan and execution of directing mediations customized to the special necessities of college understudies in the field of Estimation and Assessment.

Development and psychometric evaluation of the type 1 diabetes mellitus self-management scale for parents

Background/aimDiabetes has become a global epidemic, necessitating effective self-management strategies. This is particularly crucial for parents of children with type 1 diabetes mellitus, as they must make numerous daily decisions and perform complex care activities. Therefore, the aim of this study was to develop a comprehensive diabetes self-management scale specifically for parents of children with type 1 diabetes. This scale aims to holistically address behaviors impacting diabetes self-management and to evaluate its psychometric properties.Materials and methodsA methodological, correlational, and cross-sectional study was conducted with a sample of 190 parents of children with type 1 diabetes mellitus. The scale items were reviewed by five experts to ensure they adequately covered the parents' evaluation of their children's diabetes self-management. Following this, a Turkish language expert assessed the draft scale for language accuracy, comprehensibility, and grammar. The data were analyzed using descriptive statistics (numbers and percentages), Cronbach's α reliability coefficient, factor analysis, and correlation analysis.ResultsThe Cronbach's alpha for the overall scale was 0.893, and the Cronbach's alpha for the subscales was between 0.757 and 0.845. The item-total score correlations ranged between 0.408 and 0.660 (p < .05). The exploratory factor analysis showed that the scale explained 61.427% of the total variance, and the factor loadings of items ranged from 0.574 to 0.859. The confirmatory factor analysis also showed that the factor loadings of the scale items ranged from 0.574 to 0.859.Conclusion: The validity and reliability analyses revealed that the scale is a valid and reliable measurement tool for the Turkish culture.

The effects of VAR interventions on self-rated mental fatigue and self-rated performance of football referees

ABSTRACT Football referees are required to make numerous decisions per match under physiological and emotional strain. Within this context, VAR (video assistant referee) interventions represent a key match event in which referees’ critical decisions are challenged and thus their perceived ability to control the match might also be obscured. In this study, we investigated the effects of a VAR intervention on referees’ self-rated mental fatigue (MF) and performance. Nine elite referees completed over 2–6 real matches each (total of 44 matches) a single item of MF before the match, at half-time, and immediately after the match. Situational and performance indices were also collected. The referees’ level of MF significantly increased from pre-match to half-time to post-match, yet the referees were not completely mentally fatigued at the end of the matches. Post-match MF was higher (a medium effect) for matches with a VAR intervention (n = 9) compared with matches without an intervention (n = 35). Post-match MF correlated negatively with self-rated performance, r(44) = .35, p = .019. These findings are discussed in relation to current theory and research in the area of self-control and MF in sports. We also provide recommendations for referee training and match preparation.