The effects of both methods in decreasing anxiety were more significant for first-timers (p = 0.009 in RP/0.014 in VP). We noted high satisfaction levels for both forms of preparation. The VP technique was superior in reducing patient anxiety, especially in first-time MRI patients. Hence, VP techniques can be used in different clinical settings to reduce anxiety and facilitate patients’ understanding of the instructions given.Background: Magnetic resonance imaging (MRI) exams may cause patients to feel anxious before or during the scan, which affects the scanning outcome and leads to motion artifacts. Adequate preparation can effectively alleviate patients’ anxiety before the scan. We aimed to assess the effect of different preparation methods on MRI-induced anxiety: We conducted a prospective randomized study on MRI patients between March and May 2022. We divided 30 patients into two groups: the control group, which received routine preparation (RP), and the experimental group, which received video preparation (VP). We used the State-Trait Anxiety Inventory (STAI) to measure anxiety levels before and after the interventions. We assessed patients’ self-satisfaction after the scan: After preparation, VP (STAI mean = 10.7500) and RP (STAI mean = 12.7857), we observed a significant association between the pre- and post-STAI results in VP ( Since the 1980s, there has been a significant rise in magnetic resonance imaging (MRI), which has become one of the leading modalities used in the clinical field. MRI has evolved as one of the most important diagnostic imaging techniques. In addition, MRI is the modality of choice for imaging abnormalities due to its superior illustration and contrast of soft tissues . It is aThe slightest movement will deteriorate image quality and affect diagnostic accuracy. Thus, patient immobilization during the entire procedure is necessary to achieve high-quality images . AnxietyAnxiety is considered a reason for motion artifact and the early termination of exams, eventually affecting the scanning outcome. Thus, researchers seek to examine various strategies to reduce it. Mock MRI, sedation, and cognitive behavioral therapy are useful options to prevent anxiety. However, most of them are time- and cost-consuming, demanding, or do not apply to all patients’ informational needs . MultiplAdequate preparation by staff is one of the chief purported methods of alleviating patients’ stress . WrittenAlthough several studies conducted worldwide have shown results consistent with our hypothesis, claiming that sufficient preparation influences patients’ anxiety levels, the topic cannot be generalized broadly due to geographic and social differences . TherefoOur study aimed to measure and analyze the differences in anxiety level before and after routine and video preparation of patients undergoing Magnetic Resonance Imaging. In addition, we compared the effectiveness of different preparation methods on anxiety level of first time and non-first time MRI patients and finally assessed patients’ satisfaction regarding different types of preparations.We conducted a prospective, randomized control trial with male and female patients in the MRI department at KAAUH in Riyadh, Saudi Arabia. The 30 patients ranged in age from 18 to 60 years and spoke Arabic and English. We excluded patients who were unable to give informed consent, patients with anxiety disorders, sedated patients, inpatients, and those undergoing feet-first exams.We measured patients’ anxiety using the State-Trait Anxiety Inventory (STAI) to assess the effect of different preparation methods , after rUsing the STAI, we assessed the effects of different preparation methods on the level of anxiety in patients who were willing to undergo an MRI exam. The STAI, developed by Spielberger in 1983, consists of 40 questions measured on a 4-point Likert scale and is commonly employed in the clinical field to gauge trait and state anxiety . To reduThis short version of the STAI involves six questions that the patient should answer. According to the answers , we calculated the score. The score for the highest anxiety level was 24, while the lowest score was 6.t-test to compare the outcomes between the pre- and post-intervention tests, specifically the mean within one group where the level of significance was set at p < 0.05. We also performed an independent samples t-test to compare the mean anxiety between the two groups, along with patient satisfaction, where the level of significance was set at p < 0.05. We collected the data and tabulated them using the Statistical Package for the Social Sciences (SPSS), Version 26,  to assess the impact of the different preparation methods. We employed a paired We conducted the study at KAAUH. We obtained approval from the institutional review board (IRB No: 22-0037) of Princess Nourah bint Abdulrahman University, as well as from KAAUH (IRB No: 1-70004-024). Participation was voluntary without compensation. The participants were informed about the study’s objectives and their rights, including the right to withdraw at any time. Prior to the study, each participant signed an informed consent form, and was told that all identifying information would be kept confidential and used only for the purposes of the study.In total, 30 patients participated; 14 were in the control group and 16 were in the experimental group, VP .However, the majority of participants were female (81.3% in the VP group and 35.7% in RP group), as seen in Another goal of this study was to compare the effectiveness of different preparation methods (RP and VP) on anxiety levels between first-time and non-first-time MRI patients. p-value was less than 0.05, we concluded that there was an association between the pre- and post-STAI results in the VP group (p < 0.05). However, there was no association between the pre- and post-STAI results in the RP group (p > 0.05) because the p-value was greater than our chosen significance level (p = 0.099).As indicated in the table, since the Additional VP reduces patient anxiety before an MRI scan ,10. WithThe average STAI outcomes of the 14 MRI patients who underwent RP and the 16 patients who received VP are outlined in p > 0.05, as outlined in p < 0.05. Further, Following preparation, both groups filled out another STAI for a comparison of the influence of the two preparation methods. As demonstrated in The findings of One objective of our study was to compare the effectiveness of different preparation methods (VP and RP) on anxiety levels between first-timers and non-first-timers. As Following previous studies ,15, invop = 0.009/0.014, respectively). According to the responses, both methods were satisfactory, but patients who received VP were slightly more satisfied than those who received RP as mentioned by [After the exam, both groups were administered a questionnaire with four questions to assess satisfaction regarding the preparation methods and their MRI experience. We observed two especially remarkable findings. The bar chart in ioned by ,21.p-value was p = 0.009 and 0.014 for RP and VP, respectively, we concluded that there was an association between both types of preparation (VP and RP) and patient satisfaction (p < 0.05).As indicated in Possible answers included no (blue), slightly (red), sort of (green) and a lot (orange) (0.00 for RP and 1.00 for VP).Possible answers included no (blue), slightly (red), sort of (green) and a lot (orange) (0.00 for RP and 1.00 for VP).Our overall results support the outcomes of prior studies ,15,17, wGeneralization of the results needs to be investigated further. We had a small sample size, primarily due to time restrictions; the effects could be better understood with a larger sample size. Intervention techniques can affect the outcomes; a previous study compared cortisol and prolactin levels using blood samples from patients before and after preparation . We did This study demonstrates the effectiveness of two different preparation techniques in reducing patient anxiety before an MRI exam. We included a comparison between routine  preparation and a video that showed patients what to expect from the exam. In line with the findings of previous studies ,19, bothThe current study has several strengths. The visual preparation technique demonstrated significant reduction in patient anxiety in comparison to other preparation techniques. This visual information can be clearly understood by different individuals and also the video link is easy to apply and can be implemented routinely prior to different clinical examinations. In contrast, there are some limitations to our findings; the small sample size, exclusion of inpatient and feet-first exams and restricted data collection period.Video Preparation (VP) was effective at alleviating various degrees of patient anxiety in our experiment. VP is easy to implement in multiple areas and departments, regardless of social or geographic differences. We recommend using VP in every MR department to reduce anxiety and to make it easier for patients to understand instructions. We also suggest applying the experiment to a broader range of the population and that future studies include bigger samples and diverse cases, with no exclusions.


Nowadays, digitalization and automation in both industrial and research activities are driving forces of innovations. In recent years, machine learning (ML) techniques have been widely applied in these areas. A paramount direction in the application of ML models is the prediction of the material service time in heating devices. The results of ML algorithms are easy to interpret and can significantly shorten the time required for research and decision-making, substituting the trial-and-error approach and allowing for more sustainable processes. This work presents the state of the art in the application of machine learning for the investigation of MgO-C refractories, which are materials mainly consumed by the steel industry. Firstly, ML algorithms are presented, with an emphasis on the most commonly used ones in refractories engineering. Then, we reveal the application of ML in laboratory and industrial-scale investigations of MgO-C refractories. The first group reveals the implementation of ML techniques in the prediction of the most critical properties of MgO-C, including oxidation resistance, optimization of the C content, corrosion resistance, and thermomechanical properties. For the second group, ML was shown to be mostly utilized for the prediction of the service time of refractories. The work is summarized by indicating the opportunities and limitations of ML in the refractories engineering field. Above all, reliable models require an appropriate amount of high-quality data, which is the greatest current challenge and a call to the industry for data sharing, which will be reimbursed over the longer lifetimes of devices. They thermally protect basic oxygen furnaces (BOFs), steel ladles, and electric arc furnaces (EAFs), and they are used in the production of special products, like purging shapes and taphole sleeves . The wea–1750 °C ,4. In st–1750 °C . Refract–1750 °C . Another3AlC2, Ti3SiC2, Cr3C2C, spinel micro-powder, YAG nanopowder, and other oxide composites) [Although MgO-C refractories have been used since 1950 in steel and refining plants , their sposites) ,23,24,25posites) ,27,28, fposites) ,30, sessposites) ,32,33, cposites) ,35,36,37posites) , and obsposites) . Recentlposites) ,41,42. Wposites) ,44,45,46posites)  obtainedHowever, the commonality in all these experimental studies is that both the experiment and result interpretations are always conducted in a traditional way, with a relatively low quantity of data taken for analysis. For MgO-C refractories, researchers are focused on very detailed investigations of the mechanisms responsible for the particular hot-temperature behavior of MgO-C bricks. Simultaneously, researchers have to face the high quantity of various data types . For thiRecently, more companies have become interested in collecting data and finding relationships with refractories’ wear rates to optimize the process and make it more efficient as well as environmentally friendly. The implementation of Industry 4.0  has creaAccording to Pilania’s work , ML algoTaking into account the relatively newly applied ML techniques in the refractories field and their vast innovation potential, this work aims to evaluate the most important published works on the application of various machine learning techniques in the investigation of MgO-C refractories. This review is divided into three main parts. Firstly, in Machine learning is a subset of artificial intelligence. Algorithms are dedicated to building computational tools that make decisions without explicit coding. One of the main aims of the application of ML algorithms is taking the historical data and training the algorithms to further use these data in the prediction of specific features. The main advantage of ML algorithms is their powerful performance and speed of data processing compared to hand-coding. ML algorithms have proven their performance and utility in a variety of fields, such as speech recognition, text mining, medicine, data analysis, aeronautics, data analysis, stock market analysis, and many others ,53. ThisSarker  has diviJain and Kumar  describeAccording to Sarker , the algOxidation of carbon in MgO-C refractories, especially below 1400 °C, is one of the main problems in the application of these materials . The dec2 in the range 0.986–0.999. Finally, the three-layer back propagation ANN model was used to predict the oxidation kinetics of MgO-C specimens based on their weight loss at different temperatures. The authors developed reliable models with excellent fit between experimental and calculated data. The oxygen diffusion was reported as responsible for carbon oxidation in MgO-C refractories.Artificial neural networks (ANN), which represent one of the supervised ML techniques  and to provide appropriate corrosion resistance for refractories. It is worth noting that only seven observations (measured area loss) were used for model testing. Probably, the low number of measurements (both in the training procedure and testing) is the reason why the percent prediction error is high for some observations . The presented research included only a few factors influencing the refractories’ corrosion resistance. If the model is allocated for direct use in steel plants, more factors should be included, e.g., number of heats/day, slag chemistry, tapping temperatures, secondary treatment temperatures, types of additives used for refining, etc. Moreover, a greater number of samples should be tested to provide higher accuracy of the model .MgO-C refractories are exposed to extreme thermal, mechanical, and chemical stresses during operation in the steel plant. The highest thermal stresses occur during the preheating stage of the heating device and the tapping of the hot molten steel into the ladle, where the refractory lining suffers mostly from a high temperature gradient (from about 300 °C at the shell to 1600–1700 °C at the lining). For some applications, MgO-C materials have to withstand additional mechanical stresses, e.g., due to rocking of the BOF vessel during preheating ,76.An advanced investigation of the thermomechanical behavior of different lining concepts in steel ladles was conducted by Hou et al. . ArtificThe optimal ANN architecture was found for seven nodes in the hidden layer and Bayesian regularization with 160 samples for training. Two insulation lining concepts .Borges et al.  applied 2 of 0.8927 for analyzing predicted vs. actual values of model output. During testing of the model, the coefficient of determination for actual and predicted ladle service times was R2 = 0.66. The developed model was used to simulate the ladle service time for a wide range of values of selected properties . The authors showed that if the tapping temperature increases the ladle service time decreases (R2 = 0.9719), and, similarly, if electrical energy consumption increases during secondary treatment the service time of the ladle decreases (R2 = 0.9860). According to the presented studies, the authors show that, if the time of the full ladle  increases, the ladle service time increases (R2 = 0.9249); similarly, if Ar consumption increases, the ladle service time increases (R2 = 0.9945). Furthermore, the authors extracted the selected variables’ importance and their influence on ladle service time using a developed criterion. Among the seven selected variables, the most negative impacts on ladle service time were from the time with an empty ladle (assumed importance coefficient of −21.08%), electrical energy consumption (−20.43%), and the number of heats with vacuum treatment (−15.71%). The most positive influences on ladle service time were associated with a higher amount of consumed Ar (+15.20%) and a longer time with the ladle filled with metal (+9.71%). Based on the obtained models [Another industrial study concerning ladle treatment was conducted by Jančar et al. . The autd models , the aut2 = 0.66). A probable reason for this is associated with the types of MgO-C materials installed in the ladle. As the authors indicated [The authors of  used a lndicated , 106 ladYemelyanov et al.  proposedIt is worth emphasizing that the authors used unprecedented real thermograms of the lining collected at the steel plant. In total, 480 standard thermograms and 620 collected experimentally were used, which made it possible to obtain a reliable model with low values of classification error (0.258–0.443). The model performance was satisfactory and was the basis for developing specialist software for lining condition diagnosis. The major advantage of this work is that its results are positively implemented in industry. Moreover, it seems that the model is flexible and can be used successfully in various types of devices, e.g., steel ladles and torpedo ladles.up is the upper boundary of the wear class, t is the class number (1…10), and w is the maximum value of the wear index. ANN was used to predict the wear class depending on selected metallurgical parameters. The quality of training was 64.56% and 66.21% with testing performed using the R programming language. Zelik et al.  showed t2 (coefficient of determination), MAPE (mean absolute percentage error), and MAE (mean absolute error), as shown in An extension of this work  was showTwo works ,85 descrAlthough the number of publications on the application of machine learning is rapidly growing, with a 56% increase over the last 2 years, it is still very low when it comes to ML application in the refractory industry. Based on reviewed works ,83,84,85ML techniques currently have obvious limitations, as the quality of data collected in the industry is still not satisfactory. Thus, it is necessary and highly recommended to improve the process of data registration, especially data involving steel production processes, to avoid missing data, unreal values, or mistake-generative hand typing. Using data of unsatisfactory quality may lead to inaccurate conclusions.Another important limitation is related to laboratory experiments and the fact that ML algorithms are trained on data collected from specific, highly advanced examinations. It might be difficult to apply external data to such models and obtain reliable results, especially if one—allegedly insignificant—factor is changed.Nevertheless, interest in using ML techniques in the refractory industry is growing, as it seems that high digitalization in this area is unavoidable. The possibility of predicting the wear rate of refractories depending on their metallurgical data will be especially encouraging for refractory end-users. They should be conscious of the need to improve data collection in order to develop highly predictive models that will serve in industrial practice and help to make the steel process more sustainable.The current state of knowledge on ML techniques—relatively newly applied in refractories investigation—was reviewed in this work for MgO-C materials, which constitute over 70% of total refractories production. The most commonly used ML algorithm type is currently artificial neural networks. The clustering algorithm is also effectively applied in the optimization of MgO-C materials and the identification of factors influencing vessels’ service time in steel production.Nevertheless, the number of papers on the application of ML techniques is still insufficient considering the rapidly growing interest in and high potential of ML techniques. The limited accessibility of reliable data is one of the reasons, which results from the disclosure politics of steel plants. The end users of MgO-C refractories will be conscious of the benefits gained from building high-quality ML models, which can influence the extension of the service time of refractories, thus making the steel production process more efficient and sustainable.Concerning experimental research activities on MgO-C refractories, it is always cost-intensive to prepare and analyze the great number of samples demanded for ML implementation. The experimental approach has been changing, and wide implementation of ML in the refractory industry is unavoidable to speed up innovation in the industry in the near future, which stands at the front of a fast-changing and challenging environment.


Medical education is one of the most sought-after disciplines for its prestigious and noble status. Institutions endeavor to identify admissions criteria to register bright students who can handle the complexity of medical training and become competent clinicians. This study aims to apply statistical and educational data mining approaches to study the relationship between pre-admission criteria and student performance in medical programs at a public university in Saudi Arabia. The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The current pre-admission criterion is the admission score taken as the weighted average of high school percentage (HSP), general aptitude test (GAT) and standard achievement admission test (SAAT), with respective weights of 0.3, 0.3 and 0.4. Regression and optimization techniques have been applied to identify weightages that better fit the data. Five classification techniques—Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors—are employed to develop models to predict student performance. The regression and optimization analyses show that optimized weights of HSP, GAT and SAAT are 0.3, 0.2 and 0.5, respectively. The results depict that the performance of the models improves with admission scores based on optimized weightages. Further, the Neural Network and Naïve Bayes techniques outperform other techniques. Firstly, this study proposes to revise the weights of HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. Secondly, as the evaluation metrics of models remain less than 0.75, this study proposes to identify additional student features for calculating admission scores to select ideal candidates for medical programs. The medical profession is a pillar for the well-being of any society. Clinicians play a pivotal role in keeping society healthy and safe against diseases and disasters. Therefore, medical institutions strive to attain the best admissions criteria to recruit medical professionals. One study  identifiAt the onset of medical education, the selection criteria for admission to Saudi medical programs are predominantly based on high school performance coupled with written tests and interviews at some institutions . CurrentA study  based onMoreover, the latest studies done at King Saud University in 2021 used correlation coefficient and regression analysis to analyze the relationship between GPA, HSP, SAAT and GAT. The study found a significant positive association between pre-admission student scores and their academic performance . SimilarThese previous studies employed formal statistical analysis to study the relationships of pre-admission criteria with the academic performance of medical students. More recently, a new discipline has evolved: educational data mining (EDM), a branch of artificial intelligence and machine learning inspired by brain sciences. “Formal statistical inference is assumption driven in the sense that a hypothesis is formed and then tested against the data. Data mining, in contrast, is discovery driven in the sense that the hypothesis is automatically extracted from the data” . DifferiEDM is “concerned with developing methods for exploring the unique types of data that come from educational environments. It can be also defined as the application of data mining (DM) techniques to this specific type of dataset that come from educational environments to address important educational questions” . EDM empOne recent study  gives anMost existing EDM research has applied clustering, classification, association rule mining, and text mining into educational data ,24,25. TA prediction model infers a single aspect of the data from a combination of other data aspects . A fundamental step in applying a predictive data modelling approach is the selection of features that optimally predict the desired outcome. An article  presentsThis study aims to apply the EDM approach to study the relationship of pre-admission criteria with student performance in medical programs at King Khalid University in Saudi Arabia. The university is situated in the Aseer region in the southern part of the country. It currently has 4171 employees, 3588 faculty members, 29 accredited colleges and 120 departments. A total of 54,291 students are enrolled in diploma, bachelor, master and doctoral programs . The uniAssess the efficacy of weightages given to HSP, GAT and SAT using regression and optimization techniques.Compare the performance of selected data mining techniques to predict student performance based on existing admission criteria.Recommend revised weightages for admission criteria based upon comparative analysis of data mining techniques using existing weightages and optimized weightages to predict student performance.Further, the study has identified the following objectives in order to answer the research question with the help of EDM techniques: The remainder of the paper has been organized into following sections: materials and methods, results, discussion, and conclusion.The present study is a retrospective cohort study conducted at the College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia between February and November 2022. The admission and registration deanship provided anonymized data for the students enrolled in the five academic years 2015–2016 to 2019–2020. The deanship has made these data available on the university portal as open data to be used by other researchers.As per the research question and objectives of the study, firstly a correlation was identified to establish the relationship of HSP, GAT, SAAT and admission scores with GPA. Thereafter, linear regression and optimization was carried out to identify the optimized weightages of HSP, GAT and SAAT to be used in admission score calculation. Thereafter, different classification techniques of EDM were applied. Two sets of models were established, first on the basis of current admission scores, and second with the optimized admission scores. Finally, on the basis of comparative analysis of two different sets of models, revised weightages are proposed. The following steps were employed to develop and compare the classification models using different data mining techniques: data pre-processing, data scaling and outlier handling, correlation analysis, linear regression and optimization, data partitioning, model fitting and assessment, and model comparisons; Data pre-processing involves loading data, identifying class distribution and handling missing values. Four variables are loaded: HSP, GAT, SAAT and GPA. As expected, the brightest students are enrolled in the medicine program, so their GPA is high. However, the class imbalance problem in educational datasets could hamper the accuracy of predictive models as many of these models are designed on the assumption that the predicted class is balanced . TherefoPowerTransformer function from the sklearn library [Because the numeric data belonged to different ranges, it was essential to scale the data to a common range. Scaling was done with the help of the  library . This fu library  and Yeo– library . By usin library ,37. Ther library . This mepearsonr from the scipy.stats package. It was calculated between HSP, GAT, SAAT, ASC and admission score optimized ASO, and GPA. The methodology of calculation of ASO is described in the next p-value. Further, the standard error was calculated using Equation (1). As the sample size (N = 954) is large, Equation (1) may be used to compute the standard error, where r is the correlation coefficient [The Pearson correlation coefficient was calculated using the Python  functionfficient .(1)StanTwo types of models were developed, one with the current admission criteria and the other with optimized weightages of HSP, GAT and SAAT. Therefore, linear regression and optimization techniques were applied to identify the best weights for each admission criterion. The admission scores were recomputed with the optimized weightages derived in linear regression and optimization process, and named the “admission score optimized” (ASO). More details of the Python functions applied are presented in the results The feature variable admission score was computed through the weighted average of HSP, GAT and SAAT with the current weightages of 0.3, 0.3 and 0.4, respectively, and named the “admission score current” (ASC). The dataset was then partitioned into two sets of training and testing in a ratio of 80:20, with one feature as ASC and the target variable as GPA. This step was repeated after step 2.4 for ASO-based models. Five techniques—Decision Tree, Neural Network, Naïve Bayes, K-Nearest Neighbors  and RandTrue positive (TP): the number of cases correctly predicted as positive,False positive (FP): the number of cases incorrectly predicted as positive,True negative (TN): the number of cases correctly predicted as negative andFalse negative (FN): the number of cases incorrectly predicted as negative.Four metrics—accuracy, recall, precision, and the F1 score ,43—are uAccuracy—representing the effectiveness of the model—is the percentage of correctly predicted results, as per Equation (2). Recall, or sensitivity, is the percentage of correctly predicted positives to total positives, as per Equation (3). Precision, which accounts for the predictive power of the model, is the percentage of correct positive observations, as per Equation (4). F1 score is the harmonic mean of the recall and precision, a measure that balances recall and precision, as per Equation (5).These four criteria were used to compare the performance of all five models developed using data mining techniques, namely Decision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors. Similarly, comparison were made between both sets of models based on ASC and ASO feature variables.All admission criteria—HSP, GAT and SAAT—show a significant correlation with GPA. Nonetheless, GAT has the lowest correlation coefficient . SimilarLinearRegression function of the sklearn package and minimize routine of the scipy.optimize package, respectively. The LinearRegression function was used to define the function to be used in minimize routine. This function was set to compute and return the mean squared error (MSE) by fitting admission scores with GPA. The minimize function iterated the values of weightages with the boundary condition set between 0.2 and 1.0 for all variables . This resulted in new weightages of 0.311, 0.200 and 0.489 for HSP, GAT and SAT in sixteen iterations. Furthermore, the value of the MSE decreased from 0.7225 to 0.7020. Therefore, the admission score optimized (ASO) was estimated using the weighted sum of HSP, GAT and SAAT with the weightages of 0.3, 0.2 and 0.5, respectively, rounded to one decimal place for simple implementation. In essence, this proposal translates to a 33.33 percent decrease in the weightage of GAT and a 25 percent increase in the weightage of SAAT from the current values. The value of HSP remains unchanged.The feature variable admission score current (ASC) was computed using the weighted average of HSP, GAT and SAAT with the current weightages of 0.3, 0.3 and 0.4, respectively. The scores were also computed with new weights derived from the regression and optimization techniques. The regression and optimization techniques were run in Python  using thDecision Tree, Neural Network, Random Forest, Naïve Bayes and K-Nearest Neighbors were run using ASC as the feature variable and GPA as the target variable. Model evaluation metrics such as accuracy, recall, precision, and the F1 score were estimated using Python’s metrics class from the sklearn package. The comparative charts in The models were run using ASO as the feature variable and GPA as the target variable. Further, Education is one of the cardinal pillars of any nation that unassailably aids in the acceleration of the nation’s economic development. The recent advancement of technology, especially information technology, in education systems has resulted in an enormous amount of data (“Big Data”) that could be exploited to understand student behavior and progress in acquiring relevant knowledge, as well as their possible future contribution to society. Data can also be used to evaluate the effectiveness of a teacher in molding students’ knowledge-acquiring behavior. Similarly, students’ pre-admission criteria may also be correlated to their educational performance to evaluate the effectiveness of the criteria. p-values are less than 0.05 and the standard errors are below 0.03. Therefore, all the pre-admission criteria such as HSP, GAT and SAAT may continue to be important factors for calculating admission scores. However, the results of linear regression and optimization techniques show that the weightages allocated to HSP, GAT and SAT are not optimal. The MSE was used to identify optimal weightages. It reached the lowest value of 0.7020 in 16 iterations while keeping the lower boundary values of these variables at 0.2. This resulted in optimized weightages of 0.311, 0.200 and 0.489 for HSP, GAT and SAT, respectively. Finally, the new proposed weightages are 0.3, 0.2 and 0.5 for HSP, GAT and SAT, respectively, rounded to one decimal place for simple implementation.This study first confirms the significant correlation of all variables—HSP, GAT, SAAT, ASC and ASO—with GPA, as the Furthermore, the models fitted with optimized weightages derived through linear regression and optimization techniques outperform the models fitted with current weightages for each technique. Therefore, this study proposes to revise the weightages for HSP, GAT and SAAT to 0.3, 0.2 and 0.5, respectively. The proposal to reduce the weightage of GAT by 0.1 and increase that of SAAT by 0.1 is consistent with the results of previous studies ,5,6,7 whSecond, for the comparative assessments, Naïve Bayes performs best for current weightages, followed by Neural Network and K-Nearest Neighbors. Decision Tree and Random Forest yield similar performance and produce the lowest evaluation metrics. Neural Network performs best for optimized weightages, followed closely by Naïve Bayes. K-Nearest Neighbors, Decision Tree and Random Forest yield similar performance and produce lower values of evaluation metrics. The outperformance of Neural Networks is well-established in the literature ,45. The evaluation metrics for the best-performing technique, Neural Networks, reached acceptable levels for accuracy (effectiveness), precision (predictive power), recall (sensitivity) and F1 Score , with values of 0.743, 0.749, 0.750 and 0.743, respectively. Nonetheless, there is room to improve the performance of classification through identifying additional constituents for admission scores in addition to HSP, GAT and SAAT. This study advises identifying and incorporating additional aspects of prospective students for computing admission scores to improve the relationship with GPA and improve the metrics of models.This study applied educational data mining techniques, a subfield of artificial intelligence and machine learning inspired from brain sciences, to assess the efficacy of pre-admission criteria to predict the performance of medical students vis-à-vis GPA. The existing features HSP, GPA and SAAT show significant correlation with GPA. However, the study proposes to revise the weightages of HSP, GAT and SAT to 0.3, 0.2 and 0.5, respectively, estimated through the regression and optimization technique in calculating the admission score. These optimized weightages improve the performance of different EDM models compared to the existing weightages of HSP, GAT and SAAT of 0.3, 0.3 and 0.4. In essence, this proposal translates to a 33.33 percent decrease in the weightage of GAT and a 25 percent increase in the weightage of SAAT from the current values, while the value of HSP remains unchanged. Further, the study identifies that Neural Network and Naïve Bayes techniques produce similar results and outperform other techniques. Because the metrics of the models remain less than 0.75, the university may identify and incorporate additional student features to improve the prediction metrics of the models. This research has limited the features to only HSP, GAT and SAAT as per the current university policy. Future research may expand the features to include non-cognitive and demographic factors. Additionally, future studies may apply other brain sciences techniques such as dynamic neural network model types to predict or classify student performance. This will help to identify more capable and deserving students for medical programs.


Our study suggests that patients with nodular scleritis have a lower risk of having an associated immune-mediated disease.(1) Purpose: A patient with scleritis may have an associated systemic disease, which is often autoimmunological and seldom infectious in origin. The data regarding such associations in Hispanic populations are scarce. Therefore, we evaluated the clinical characteristics and systemic-disease associations of a cohort of Hispanic patients with scleritis. (2) Methods: A retrospective review of the medical records (January 1990–July 2021) of two private uveitis practices in Puerto Rico was performed. Clinical characteristics and systemic-disease associations observed either at presentation or diagnosed as a consequence of the initial workup were recorded. (3) Results: A total of 178 eyes of 141 patients diagnosed with scleritis were identified. An associated autoimmune disease was present in 33.3% of the patients . An associated infectious disease was present in 5.7% of the patients . One patient had all-trans retinoic-acid-associated scleritis. Statistical analysis revealed that patients with nodular anterior scleritis were less likely to have an associated immune-mediated disease (OR: 0.21;  Scleritis is an inflammatory ocular disorder within the scleral wall of the eye . It has The diagnosis of scleritis is made clinically, based on the patient’s comprehensive medical history, clinical exam, and seldom used ancillary imaging techniques, such as B-scan ultrasound . The mosTo the best of our knowledge, there are no reports in the medical literature characterizing a cohort of scleritis patients of Hispanic origin in Latin America, including Puerto Rico. The aim of our study was to describe the demographic and clinical characteristics (upon presentation) of a cohort of Hispanic scleritis patients living in Puerto Rico.We performed a retrospective review of medical records generated from January 1990 through July 2021; file records were related to scleritis patients from two private uveitis practices and the pertinent outpatient clinics of the Department of Ophthalmology of the University of Puerto Rico School of Medicine. The scleritis diagnoses of these patients were made by certified uveitis specialists who assessed a combination of characteristic clinical findings, history of present illness, and past medical history. Scleritis was often differentiated from episcleritis by the application of 10% phenylephrine drops. Scleritis included edema that affected the episcleral and scleral tissues and congestion of superficial and deep scleral vessels; of the two, only congestion of deep scleral vessels remains after the application of phenylephrine drops. Scleritis was classified as diffuse anterior, nodular anterior, necrotizing, or posterior, according to the definitions set forth by Watson and Hayreh. All the patients underwent a comprehensive physical examination and an extensive systemic workup to identify scleritis-associated conditions such as rheumatoid arthritis, systemic vasculitis, systemic lupus erythematosus (SLE), syphilis, and viral etiologies. The ascertainment of an associated immune-mediated or infectious disease at a given patient’s presentation was made by a review of that individual’s clinical history, an examination, and a clinical diagnosis supported by ancillary laboratory testing. Consultations by an internal medicine specialist or rheumatologist were sought when needed, on a case-by-case basis. The previously identified conditions were included in each patient’s history if that person had been diagnosed before the initial visit (within 1 month) or because of the initial medical workup order for the scleritis diagnosis. Patient demographics such as age, sex, race, and ethnicity were recorded.Furthermore, the data compilation included visual acuity (VA) and intraocular pressure (IOP) at presentation, ocular complications such as keratitis and uveitis, past medical (including ocular) history, and a laboratory workup. This workup included immunological and serological tests that looked at HLA-B27 positivity, antineutrophil cytoplasmic antibody levels (ANCA), rheumatoid factor (RF), antinuclear antibody (ANA), cyclic citrullinated peptide, the Sjögren’s antibodies SS-A (anti-Ro) and SS-B (anti-La), rapid plasma reagin, fluorescent treponemal antibody absorption (FTA-ABS), purified protein derivative, QuantiFERON-Tb, herpes simplex virus (HSV)-1 and -2 IgG and IgM, and herpes zoster IgG and IgM; a chest X-ray was performed as well.The data obtained from the review of the medical records of the patients with scleritis who met the study criteria were prospectively entered into a new database for analysis. The database included demographic and clinical data, past medical history, associated immune-mediated systemic and/or infectious diseases, and laboratory workups. Descriptive statistical analysis was performed using the Microsoft Excel software program. The frequencies of clinical and demographic variables were tabulated to facilitate said analysis.p value of 0.05 was considered significant. The corresponding confidence intervals and odds ratios of each potential risk factor were included in the analysis. The University of Puerto Rico Medical Sciences Campus Institutional Review Board reviewed and approved this protocol.Fisher’s exact test was utilized to analyze significant risk factors for infectious disease or immune-mediated disease using the statistical software program JMP. A A total of 141 patients with scleritis were identified over a 31-year period. Patient demographic and clinical characteristics have been summarized see . The patNearly 40% of the patients with a scleritis diagnosis also had an immune-mediated systemic or infectious disease. The presence of infectious diseases in patients with scleritis has also been discussed in the literature. In our patient population, nearly 6% of the patients had an active infectious disease at the time of their presentation of scleritis. Two patients had an untreated infection of syphilis and positive FTA-ABS. The third patient with a syphilis diagnosis had a negative FTA-ABS test but a diagnosis of HIV, which impacts the possible treatment for scleritis. Several patients had scleritis associated with either herpes simplex or herpes zoster ophthalmicus; in patients with herpes, the type was confirmed.p = 0.011) was the only type of scleritis seen less often in patients with an immune-mediated disease. No other significant risk factors were found.The risk factors for infectious and immune-mediated diseases in patients with scleritis are shown in Our study focused on the characteristics at presentation of 141 Hispanic patients with scleritis. In our cohort, all the patients identified as Hispanic, were diagnosed with a type of scleritis and evaluated for an associated systemic disease of autoimmune or infectious origin. Our results were similar to those observed in several previous series in terms of both demographic characteristics (sex and age) of our sample population and the bilaterality of their condition at presentation.In our population, females were more likely than males to be affected 70.2%). This is similar to what has been reported by other studies, in which female patients made up 60% to 74% of the total sample. In addition, our population was comparable to that of Akpek et al., in whose study 70.3% of the sample identified as women %. This i,6. The mOcular complications at presentation, such as uveitis, keratitis, decreased VA, and increased IOP (greater than 21), were measured. It is difficult to compare our population with the populations of other series because each study has its own time frame for evaluating ocular complications. Furthermore, different studies define decreases in VA in their own manner. For example, Sainz de la Maza et al. and Jabs et al. both defined decreased VA as loss of two Snellen lines at the end of the follow-up period. Compared to what was observed at the initial presentation in Sainz de la Maza’s study, 15.8% of the patients had experienced this loss by the end of the follow-up period, while in Jabs et al.’s study, it was 15.9% of the patients [Though we searched the medical literature, we could not identify any other studies that had done a retrospective analysis of patients who identified as Hispanic and had a diagnosis of scleritis. However, Lin et al. performed a study on systemic autoimmune disease in patients with idiopathic scleritis; the study included a population in which 16% of the patients identified as Hispanic . It was As stated above, previous studies have reported that 40% to 50% of patients with scleritis have an associated infectious or immune-mediated systemic disease; 5% to 10% of said patients have an infectious disease as the origin, and 30% to 40% have an associated immune-mediated systemic disease ,4. In ouOther immune-mediated systemic diseases seen in our population include Sjögren’s syndrome, RP, SLE, sarcoidosis, and granulomatosis with polyangiitis. More recent studies in the field have reported an association between Sjögren’s syndrome and scleritis ,12. In oWe observed RP in 2.8% of our patients, while 1.6% and 3.1% of the patients in Akpek et al.’s and Jabs et al.’s studies were reported to suffer from RP and scleritis, respectively . It has While multiple series have reported expected immune-mediated systemic diseases such as rheumatoid arthritis, RP, SLE, and vasculitis, our series also includes patients with scleritis that had a sarcoidosis diagnosis. Dursun et al. discuss how scleritis was the first manifestation of sarcoidosis in two patients, highlighting the importance of identifying this association in our population . In thosWhile series such as those of Akpek et al. and Jabs et al. found herpes zoster ophthalmicus (4.5% to 5.2%) and herpes simplex (1.6% to 2.1%) to be the most common infectious diseases in patients with scleritis, our patients most commonly presented with syphilis as the associated infectious disease . HoweverA statistical analysis of independent variables such as age, sex, laterality of scleritis, type of scleritis, and ocular complications as risk factors for associated immune-mediated or infectious diseases was performed. Nodular anterior scleritis was statistically significantly less likely to present in patients with immune-mediated disease. Akpek et al.’s series found that men were more likely than women to have an infectious disease, while women were more likely than men to have an immune-mediated disease; we did not see this pattern in our series . FurtherLastly, multiple cases of scleritis in patients prescribed medications, such as bisphosphonates, topiramate, and etanercept have been reported ,20,21. TAs with all retrospective studies, the data must be interpreted with caution as the nature of the study may have introduced ascertainment bias. The study was limited to two uveitis specialists’ practices, and referral bias may have been introduced. Because our study took place in clinics with uveitis specialists, its results may not accurately represent the general Hispanic population in Puerto Rico. It is also possible that more straightforward cases, particularly those associated with less severe systemic disease, may not have been referred. It is also possible that more straightforward cases, particularly those associated with less severe systemic disease, may have yet to be referred. Additionally, the number of patients in some groups, such as those with an associated infectious disease, is small, limiting statistical power for odds ratio analyses. Further studies should be performed to analyze the associations between systemic diseases and specific types of scleritis and response to treatment.


Health counseling is a prevention and health promotion action, especially in the context of a pandemic, for both preventing disease and maintaining health. Inequalities may affect receipt of health counseling. The aim was to provide an overview of the prevalence of receiving counseling and to analyze income inequality in the receipt of health counseling. Methods: This was a cross-sectional telephone survey study with individuals aged 18 years or older with diagnosis of symptomatic COVID-19 using RT-PCR testing between December 2020 and March 2021. They were asked about receipt of health counseling. Inequalities were assessed using the Slope Index of Inequality (SII) and Concentration Index (CIX) measures. We used the Chi-square test to assess the distribution of outcomes according to income. Adjusted analyses were performed using Poisson regression with robust variance adjustment. Results: A total of 2919 individuals were interviewed. Low prevalence of health counseling by healthcare practitioner was found. Participants with higher incomes were 30% more likely to receive more counseling. Conclusions: These results serve as a basis for aggregating public health promotion policies, in addition to reinforcing health counseling as a multidisciplinary team mission to promote greater health equity.The online version contains supplementary material available at 10.1186/s12889-023-15914-2. The COVID-19 pandemic has caused notorious social, economic and health impacts . AccordiKnowledge gained from previous pandemics has shown that care measures such as hand hygiene, mask use, social distancing and vaccination are effective measures for slowing the spread of viruses . In addiCounseling is a powerful and recognized effective health promotion practice for adherence to healthy behaviors and habits, and consequently, for improving people’s quality of life, and it is a comprehensive process of listening, understanding, support, and individual or collective help , 9. StudIn this sense, given that counseling is an indispensable action for prevention and health promotion, this study aims to provide an overview of the prevalence of receiving counseling and analyze income inequality in receiving counseling for measures to protect and combat the new coronavirus , counseling on a healthy lifestyle  in individuals with covid-19 in a city in southern Brazil.This is a cross-sectional study carried out in the city of Rio Grande in the far south of the state of Rio Grande do Sul/RS. Rio Grande is a port city, covering 2,817 km², with a population of 212,881 inhabitants [Our target population consisted of all individuals aged 18 years or older, infected between December/2020 and March/2021, diagnosed with covid-19 by RT-PCR, living in the urban area of the city of Rio Grande , who presented symptoms during infection and were followed by the epidemiological surveillance of the municipality. We excluded individuals living in rural areas, without cognitive capacities to answer the instrument and with no caregiver/guardian to answer for them, without telephone contact and address available in the epidemiological surveillance register, deprived of freedom, and who were no longer residing in the city during data collection. Individuals who were not located after five attempts  were considered losses and individuals who denied participating in the interview were considered refusals.Contact was made with the epidemiological health surveillance service of the municipality of Rio Grande to identify adults and elderly people infected with COVID-19 in the investigated period, thus creating a list of individuals with positive RT-PCR and their respective data . Data collection by telephone was done based on the list of individuals who were eligible for the study. Data collection was carried out by previously trained interviewers, who underwent a selection process and later underwent training and qualification that lasted for a total of 24 h. In addition to telephone interviews, home visits were offered, for face-to-face data collection, if necessary.After reading the Free and Informed Consent form, individuals who voluntarily agreed to participate in the research were interviewed. Data collection took place from July to October 2021. Questionnaires were collected electronically (tablet) using the REDcap program and using smartphones for telephone calls. Calls were recorded to ensure the safety of the researcher and interviewee, using a free cell phone application , stored in an e-mail account. The questionnaire took approximately 20 min to answer.The questionnaire contained questions on demographic information , socioeconomic status , previous diseases  and use of health services .Participants were asked about receiving health counseling for: (1) hand hygiene, mask use, social distancing; (2) practicing physical activity; (3) healthy eating; (4) sleep habits; and (5) vaccination against COVID-19. The questions relating to these variables were asked as follows, with the reply options being “yes” and “no”. “Since you were infected with COVID-19 until now, have you received any information or recommendations from healthcare practitioner  about:Preventive measures and measures to combat the novel coronavirus (Sars-CoV-2), such as: hand hygiene, use of a mask, social distancing?Practicing physical activity?The importance of maintaining a healthy diet ?Maintaining healthy sleeping habits such as sleeping at least 7 h a night?The importance of vaccination against COVID-19?”Three synthetic outcomes were built based on the above individual outcomes, as follows:Healthy lifestyle counseling ;Counseling on COVID-19 prevention ;Counseling for all five of the above guidelines.As such, eight counseling outcomes were considered in the analyses: (1) preventive measures and measures to combat the novel coronavirus; (2) practicing of physical activity; (3) importance of maintaining a healthy diet; (4) maintaining healthy sleeping habits; (5) importance of vaccination against covid-19; (6) healthy lifestyle; (7) covid-19 prevention; and (8) receiving all five items of counseling. Each outcome was dichotomized into “yes” and “no” and, for outcome 8, “yes” was considered to be a positive response to all outcomes.The independent variable is income in BRL − 0-1000/1001–2000/2001–4000/ 4001 or more and the adjustment variables are: demographic , socioeconomic  and nutritional . The SII represents the absolute difference, in predicted values, of an indicator of health among the most favored and least favored individuals in terms of socioeconomic indicators, taking into account the entire distribution of the stratifier through a regression model. It’s calculated as the absolute difference in health indicator value between the highest (score of 1) and the lowest (score of 0) value of the socioeconomic indicator classification. The SII ranges from − 100 to 100 p.p. (percentage points), with values close to zero expected in the absence of inequality [Descriptive data are presented as proportions and 95% confidence intervals (95%CI). We used the Chi-square test to assess the distribution of outcomes according to income. Adjusted analyses were performed using Poisson regression with robust variance adjustment. All associations with 95%CI without overlap between categories were considered statistically significant. Data were analyzed using the Stata 17.1 statistical package.A total of 4014 participants were diagnosed with covid-19 in the period predetermined by the study. After applying the selection criteria, 3822 of them were eligible for the study (192 were excluded due to incomplete data and/or not showing symptoms during the acute phase of infection). After losses and refusals , 2919 individuals were interviewed (76.4% of those eligible). Of these, 24.7% used at least one health service. The sample consisted mostly of women (58.6%), individuals between 18 and 59 years old (83.3%), of white skin color (77.9%) and who were married or living with a partner (60.6%). As for education and income, 44.2% had completed high school and 38.9% had monthly income of between BRL 1000–2000. In addition, 73.3% were overweight/obese , 34.0% for prevention of covid-19 and only 20.1% of the interviewed individuals reported receiving counseling for all five guidelines investigated , healthy eating , physical activity  and healthy sleeping . With regard to receiving counseling on covid-19 prevention and vaccination, prevalence was around 40% pro-rich . Finally, prevalence of counseling for all guidelines was also around 50% higher in individuals with higher incomes  . In relation to each item of counseling, all of them showed greater convergence for the most well-off. In the same sense, relative inequalities (CIX) were significantly higher for counseling on all items  . We found that the probability of participants receiving counseling was 30% higher among those with higher incomes, and this difference occurred progressively. As for the magnitude of inequalities in relation to income for each item of counseling, all of them showed a higher concentration among the wealthier.Recommendations related to coronavirus protective measures and measures to combat covid-19 were low, despite their being recognized as effective methods . It is iSocial isolation and the consequences associated with the covid-19 pandemic, which include reduced physical activity, increased unhealthy eating habits, and increased levels of stress that cause sleep disturbances, expose people to deleterious physical and mental effects . Due to The importance of counseling lies in the benefit of the actions themselves. Adequate nutrition through macronutrients and micronutrients helps correct organ functioning and disease prevention, in addition to having an impact on the prognosis of those infected . RegardiIn relation to social inequalities, greater vulnerability can be observed in relation to the poorest. Despite the progress Brazil has made with health indicators as a result of the country’s development and the project to universalize access to health , it stilIt is worth noting that Brazil is known to be a country with persistent and expressive socioeconomic inequalities, marked by income discrepancies at extreme levels, which infers that the country is one of the most unequal in the world . Such inIndeed, inequality is a major factor when it comes to the health of the population. Several studies, as well as those already presented and discussed, focus on evaluating the impact of these inequities regarding access to and use of health services. However, the scientific literature lacks studies that assess the magnitude of these inequalities for health promotion and disease prevention indicators. Understanding counseling as a powerful health promotion strategy, both existing policies and health references recognize its effectiveness, support its need and reinforce the commitment of all professionals to providing health education actions to the population in all care spaces, with the purpose of promoting health, preventing diseases and contributing to comprehensive and equitable care , 10, 48.According to data released by the Oswaldo Cruz Foundation (Fiocruz) in 2020, approximately 162 million individuals rely exclusively on the Brazilian Unified Health System (SUS) , 53.AddiHowever, the results found in this study should be interpreted considering their limitations and potential. Memory bias should be considered, as there may be underestimation of the prevalence of the outcomes. One of the strengths to note is that our sample of individuals with COVID-19 were diagnosed by the gold standard test (RT-PCR), with a high response rate (> 75%). In addition, this study is unprecedented in the literature and the inequality analysis allows for the identification of disparities, taking into account the entire distribution of data and not just groups at the extremes.The prevalence of counseling for preventive measures and measures to combat covid-19, physical activity, healthy eating, healthy sleep, vaccination for covid-19, healthy lifestyle, prevention of covid-19 and all the guidelines was 20.1% to 56.3%. The prevalence of all counseling increased according to income, except counseling for protective measures. The odds of receiving counseling were higher in the richest groups for healthy lifestyle, healthy eating, physical activity, healthy sleep, covid-19 prevention, vaccination and for all guidelines. There are pro-rich inequities in all health counseling with respect to income, except counseling for protective measures.These results serve as a basis for aggregating public health promotion policies, in addition to reinforcing health counseling as an assignment of the multidisciplinary team. It is important to carry out training actions for health teams so that all individuals receive the necessary guidance/counseling, especially in primary care, with a view to promoting greater equity in health.The study was carried out with financial support from FAPERGS - Research Support Foundation of Rio Grande do Sul, Brazil grant number 21/2551-0000107-0 Research Program for the SUS: shared management in health - PPSUS).Below is the link to the electronic supplementary material.Supplementary Material 1


Framed within the evolutionary framework, the Interpersonal Motivational System (IMS) theory suggests that eight distinct motivational impulses drive interpersonal human relationships, namely caregiving, social affiliation, attachment, rank-dominance, rank-submission, social play, cooperation, and sexuality. This theory has been widely applied in clinical practice, where psychopathology is viewed as the result of non-flexible or excessive activation of one system over another. Despite its clinical relevance, empirical studies aimed at measuring IMSs are scarce. This paper contributed to filling this gap by proposing a questionnaire to measure individuals’ activation of the eight IMSs. Two studies involving large samples of adults were conducted. The first study  concerned the development of the questionnaire and examination of its content validity through explorative factor analysis. In the second study , confirmatory factor analyses were performed to further refine and confirm the instrument’s factor structure. The final version consisted of 50 items. Empirical validity was established by investigating the correlations between the eight IMSs and other related measures . The findings suggest that the IMS framework can be used to understand individual differences in motivation and behavior in different social contexts. The thesis that human behavior is driven by a limited set of basic motivational factors has an old tradition in the psychological literature ,2,3. In Most of the more recent theoretical models ,5,6 are Currently, a unified theory of human motivation is lacking, and efforts to develop valid theories have led to the creation of various measurement instruments. This paper focuses on the Interpersonal Motivational System (IMS) theory developed by Liotti and collaborators ,16, posiIMSs are sets of innate rules aimed to ensure individuals’ and specie’s survival . The firThe present paper does not delve into the task of resolving the ongoing debate surrounding aspects like the purpose, operations, quantity, and mechanisms of motivational systems. Instead, our focus lies on the proposal advanced by Liotti and colleagues ,16, who (1) the attachment system aims to secure the protection of an attachment figure when one perceives oneself as vulnerable. In Bowlby’s theory , attachm(2) the caregiving system aims to protect another conspecific perceived as vulnerable . It invo(3–4) the Ranking system, which takes two forms of dominance and submission, aims to maintain the hierarchical organization of the group ,10. Thes(5) the sexual system aims to form a stable couple’s relationship. It refers to achieving sexual pleasure and reproductive success through sexual interaction ;(6) the cooperation system aims to achieve a common goal . It refe(7) the social affiliation system aims at achieving security and feelings of belongingness in social relationships. It is expressed by thoughts and behaviors related to the experience of feeling part of a group perceived as a source of protection and emotional support ;(8) the social play system aims at achieving equal interaction through explicit playfulness shared by the individual in group relationships. It could be intended as a precursor of a cooperation system .In Liotti’s approach, human functioning can be explained by referring to a wide array of motivational systems whose function is to allow individuals to achieve biosocial goals of high evolutionary value, adaptive for the individual, the social group, and the species ,28. The The operationalization of IMSs, as proposed by Liotti ,16, tookStarting from the criteria listed in the AIMIT handbook  and furtOne further proposal for a measure of IMSs was pursued by Brasini et al. , who devThe current study aimed to develop and validate a new instrument for assessing IMSs in adults, building upon previous attempts to create similar instruments described above. In the first phase, the scale was developed starting from revising the previous measure of IMSs developed by Prevete et al. , primariStudy I consisted of two steps aimed at constructing the hypothesized eight-factor questionnaire. First, we generated an item pool for each system and then explored the factor structure using Exploratory Factor Analysis (EFA) and Parallel Analysis.M = 34.38, SD = 11.37). Most participants had a bachelor’s degree or a higher degree (69%); 26.8% completed the secondary level of education, whereas very few only completed middle school (4.2%).A convenience sample of 455 Italian adults  participated in the study. The sample age ranged from 18 to 80 years . Informed consent was obtained from all participants before the administration of the survey, in accordance with the European General Data Protection Regulation (GDPR 2016/679). Inclusion criteria were to be of legal age, not have received a psychiatric diagnosis, and not be in psychotherapeutic treatment.Socio-demographic Data. Participants were asked about their sex assigned at birth, age, education level, and geographical provenience.Initial Item Pool for The Interpersonal Motivational Systems Questionnaire. The questionnaire for assessing the Interpersonal Motivational Systems  was developed as follows. We started from a scale previously developed by Prevete et al.  consistiA new set of items was generated for the attachment and cooperation systems since previous attempts failed to identify them as independent factors. To assess attachment, we decided to specifically focus on attachment relationships in adult partnerships. Although attachment style in adults is typically investigated through narration or self-report tools that explore relationships with peers, parents, and partners , we chosFor the cooperation system, we followed the theoretical framework proposed by Liotti and Monticelli , which dThen, in order to develop a concise and straightforward instrument, we selected only eight items from the original Prevete et al. questionnaire , and addAll the analyses were performed in SPSS v28  and R usNo items approached skewness > |3| or kurtosis > |10|, indicating that all items were normally distributed . The ParStudy II had two aims. The first aim was to test the factor structure of the questionnaire developed in Study I using the Unrestricted Confirmatory Factor Analysis framework , and toM = 44.77, SD = 15.71). A total of 44% of the participants had a bachelor’s degree or a higher degree; 44.1% completed the secondary level of education, whereas 11.9% only completed middle school.Data were collected in 2019 using an anonymous web-based survey. A sample of 635 adults  from several Italian regions was recruited through a convenience sampling technique, using the same procedure and criteria as applied in Study I. The sample age ranged from 18 to 80 years . The BFPortrait Values Questionnaire. The 21-item form of the Portrait Values Questionnaire PVQ–21; ,53) was  was 53])Attachment Style Questionnaire. Adult attachment styles were measured through the 40-item Attachment Style Questionnaire , and the range of 0.10–0.90 for acceptable residual error variances . Potential differences in model fit were evaluated using BIC, with lower values indicating better fit.In the second step, in order to ensure model parsimony and reduce complexity, we used the Wald test to identify cross-loadings that could be constrained to zero without significant loss in model fit, and then compared partially unrestricted and fully constrained CFA models for best fit, factor correlations, and parameter estimates. Since all models in this second step are nested in their unrestricted counterparts, differences in model fit were assessed using commonly used benchmarks of ΔRMSEA ≤ 0.015 and ΔCFI ≤ 0.010.Finally, to assess the construct validity of the IMS-Q, we computed mean scores for each of the eight and then estimated correlations between these scores and convergent measures of personality traits, basic human values, and attachment dimensions.2 = 2132.230, df = 1217; CFI = 0.920; RMSEA = 0.034, 90% C.I. ; SRMR = 0.027; BIC = 81,594.527, except for TLI, which slightly fell below the established threshold (TLI = 0.89). Also, when looking at the parameter estimates, the solution showed that four items . All items presented factor loadings higher than 0.30, and no critical cross-loadings were detected. Residual variances ranged from 0.371 (item 24) to 0.746 (item 2).The U-CFA performed on the 58 items resulting from the EFA in Study I presented an adequate model fit, χ2 = 1842.7565, df = 1082; CFI = 0.922; TLI = 0.912; RMSEA = 0.033, 90% C.I. , SRMR = 0.036, BIC = 69,680.579, ΔRMSEA = 0.002, and ΔCFI = 0.010. The standardized factor loadings and reliability indicators of the model are reported in As a second step, we moved to inspect nonsignificant cross-loadings that could be constrained to zero. Two hundred ninety-six loadings were nonsignificant and then constrained to zero. These changes did not result in a decline in fit, χ2 = 2389.191, df = 1147; CFI = 0.873; TLI = 0.864; RMSEA = 0.041, 90% C.I. , SRMR = 0.054, BIC = 69,869.585, and the change in CFI exceeds the frequently used benchmarks of ΔCFI ≤ 0.010. These findings indicated that the U-CFA model, despite having more free parameters, demonstrated a more satisfactory fit to the data than the traditional CFA model. This supports the notion that permitting the identified cross-loadings within the U-CFA model better captured the underlying structure of the data.Finally, we ran a traditional CFA and compared it to the factor structure resulting from the final U-CFA using the criteria introduced earlier. Overall, the CFA model does not achieve a reasonable fit by conventional standards, χWhen analyzing the associations between the IMSs and convergent measures, the results generally supported our hypotheses, with also some notable non-hypothesized associations . SpecifiThis study aimed to develop a new questionnaire that measures the eight IMSs as theorized by Liotti and collaborators ,16: attaMotivational systems are believed to have emerged during the evolutionary process, enabling individuals to interact with one another in an adaptive way. They are biologically based and selectively activated by specific external or internal cues. However, despite the growing interest in their adaptive function , empiricThe main steps of the study included reducing the number of items from the previous measure by Prevete et al. , generatThe factor analyses led to the definition of a questionnaire including 50 items that adequately identified the eight theorized IMSs. The questionnaire met the criteria of simplicity, clarity, and uniqueness of each dimension, and is a promising instrument for assessing how frequently and intensively individuals activate each IMS. The correlations with convergent measures were generally consistent with the content validity of each IMS. Specifically, we found that the caregiving system was most strongly associated with the personality traits of agreeableness and self-transcendence values, which supports the thesis that individuals who are highly concerned for the well-being of others perceived as vulnerable  are moreIdentifying the attachment system in isolation from others remains challenging due to the fact that both strategies of hyperactivation (eliciting anxiety/preoccupation responses) and deactivation (triggering avoidance responses) disclose the functioning of the attachment system . FurtherIt is noteworthy that the convergent associations of the two ranking dimensions, dominance and submission, were not symmetric, confirming the independence of these two systems. The only clearly symmetric association was with openness to experience (positive for dominance and negative for submission). The other associations were domain-specific. The dominance system was positively related to the value of self-enhancement, which represents the pursuit of one’s interests and relative success and dominance over others, as emphasized by Schwartz . FurtherThe social play system, as defined by Panksepp , refers The peculiar association of the cooperation system is with self-transcendence values, confirming the moral nature of this system as theorized by Tomasello , such asAlthough the present study aimed to test the psychometric properties of a new measurement tool for IMSs, the findings also offer an opportunity for some theoretical considerations. One consideration pertains to the number of dimensions related to motivational systems. We recognize that various models propose distinct classifications. This is the case, for instance, of Panksepp’s model, which hypothesizes a link between motivations and primary emotional systems shared across all mammalian species , or DwecThe current study has several key strengths, including satisfactory indexes of fit-in factor analyses that effectively supported the independence of the eight IMSs, content validity supported by convergent analyses, and a reduced number of items that facilitate the ease and practicality of using the questionnaire (see Despite these limitations, the investigation of IMS through the IMS-Q is a promising area of research that will shed light on a new area of research in personality studies and clinical practice. Future studies should further investigate the psychometric properties of the measure and test for socio-cultural and cross-cultural invariance, including age and gender. One area that requires further exploration concerns the stability and change in IMS over time. Although the theory posits relative stability of the IMS within individuals, it is also plausible that significant life events, such as psychotherapeutic processes, could alter the priority assigned to the different motivational systems and activate systems that are typically under-expressed. Conversely, it can be assumed that individuals may be fixed on a single system over time, rather than activating them all flexibly. Only longitudinal studies can provide insights into the temporal trends of IMS. Finally, since the theory of the IMS stems from the clinical observations of patients in psychotherapy, the most promising area for exploring the IMS questionnaire is its usability in clinical research. Since psychopathology may be related to unbalanced relations among motivational systems, namely the predominance of one or more motivational systems over the others, it could be crucial to investigate flexibility versus rigidity as a clinical index to evaluate mental health in individuals. Overall, these findings suggest that the IMS framework can be used to understand individual differences in motivation and behavior in different social contexts.


The COVID-19 pandemic and infection control measures caused changes to daily life for most people. Heavy alcohol consumption and physical inactivity are two important behavioral risk factors for noncommunicable diseases worldwide. The COVID-19 pandemic, with its social distancing measures, home office policies, isolation, and quarantine requirements may have an impact on these factors. This three-wave longitudinal study aims to investigate if psychological distress and worries related to health and economy were associated with levels and changes in alcohol consumption and physical activity during the two first years of the COVID-19 pandemic in Norway.via the Alcohol Use Disorder Identification Test (AUDIT-C) and the International Physical Activity Questionnaire (IPAQ-SF). COVID-19-related worries, home office/study, occupational situation, age, gender, children below 18 years living at home, and psychological distress (measured with the Symptom Checklist (SCL-10)) were included as independent variables in the model. A mixed model regression was used and presented with coefficients with 95% confidence intervals (CI).We used data collected in April 2020, January 2021, and January 2022 from an online longitudinal population-based survey. Alcohol consumption and physical activity status were assessed at all three measuring points Analysis of data from 25,708 participants demonstrates that participants with substantial symptoms of psychological distress more often reported higher alcohol consumption  and lower levels of physical activity  at baseline. Working/studying from home  and being male  were associated with higher alcohol consumption. Working/studying from home , and being older than 70 years  were related to lower levels of physical activity. The differences in activity levels between those with the highest and lowest levels of psychological distress reduced over time , and similarly the differences in alcohol intake reduced over time among those having and not having children < 18 years .These findings highlight the substantial increases in risks related to inactivity and alcohol consumption among those with high levels of psychological distress symptoms, and particularly during the COVID-19 pandemic, and increase the understanding of factors associated with worries and health behavior. These rThe pandemic caused by the coronavirus disease 2019 (COVID-19) had a significant impact on the daily lives of most people . It is lA growing literature shows that people with mental health problems may be especially vulnerable to increased drinking during a pandemic , 40. BotThe COVID-19 pandemic had a significant impact on people’s daily PA levels , 56. WitMost of the aforementioned studies on the impact of the pandemic on mental health, alcohol use, and PA mainly covered the early stages of COVID-19. Therefore, more knowledge is needed to elucidate the whole period from the early phases of the COVID-19 pandemic (spring of 2020) to the late phases when restrictions ceased in 2022. We opted to include control variables for home office/study and temporary layoffs, based on previous findings from our research group regarding the relationship between pandemic-related measures and alcohol consumption during periods of lockdown . Against2.2.1.In April 2020, we invited a representative sample of 81,170 people from a total of 224,000 adult inhabitants in the city of Bergen in Western Norway to participate in the study “Bergen in Change” (BiE-study) surveying the potential impact of the lockdown on everyday life, health, and health behaviors during the COVID-19 pandemic . The sam2.2.via email and short text messages (SMS) using the online data collection tool SurveyXact. The questionnaire included demographic information and questions about various aspects of life and health related to the COVID-19 pandemic and lockdown. The first data collection (T0) took place between April 15 and April 30, 2020. One month before T0, several restrictions  due to COVID-19 had been initiated.A set of electronic questionnaires was distributed All who participated at T0 were invited to respond to the survey at T1 where the data collection took place between December 2020 and January 2021. By this time, the restrictions had been eased slightly. Schools had reopened, and organized sports activities were slowly picking up. Restaurants and cafes were allowed to open with limited capacity. However, social distancing and the advice to avoid public transport were still maintained . In JanuThe third wave of data collection (T2) took place between December 2021 and January 2022, roughly coinciding with the end of the pandemic measures. Most of the restrictions put in place in April 2020 were lifted. Despite the easing of restrictions, people were still urged to practice social distancing and to wear face masks in shops and public institutions if it was not possible to keep a one-meter distance. The median time interval between the second and the third data collection was 12 months. In all, 10,867 (response rate 37% from T0) persons participated in the third wave.2.3.The main outcome variables in the present study were self-assessed alcohol consumption and PA. Alcohol consumption was assessed by the short version of the Alcohol Use Disorders Identification Test Consumption (AUDIT-C), which consists of the first three questions of the full AUDIT , 65. TheThe short form of the International Physical Activity Questionnaire (IPAQ-SF)  was usedPsychological distress was measured with the Norwegian-validated translation of the ten-item version of the Hopkins Symptom Checklist (SCL-10) . The parCovariates, such as gender, age, and children < 18 years at home, were measured at baseline.2.4.p < 0.05 for all analyses unless otherwise stated. In all the analyses, time was defined as the number of years from baseline.Stata/SE 17.0  was used for all descriptive and regression model analyses. SankeymaticWeighted estimates for the exposure variables are presented with their corresponding 95% confidence intervals (CI). Chi-square tests were used to test for statistically significant differences between groups of categorical variables.n = 25,708; 87% of the total sample). The exposure variables were kept constant at the baseline level in predicting the level and changes in the outcome variables. To explore whether exposure variables predicted changes in outcomes, interactions between these variables and time were added to the model. Maximum Likelihood estimation was used. All available responses to the outcome variables were included in the analyses.Linear mixed model analyses were used to investigate whether the exposure variables were associated with drinking behavior and PA at baseline and the extent to which they influenced any changes in the drinking behavior and PA from baseline (T0) to the following (T1 and T2) measuring points one and 2 years later. We based the analyses in this paper on questionnaires that had valid responses to all questions in AUDIT-C . It was conducted in accordance with guidance from data protection officials at the University of Bergen.3.3.1.At baseline, the median age of the participants was 50 years (interquartile range (IQR) 36–63), 56% were women, 40% had more than 3 years of university or college as their educational attainment, 94% were Norwegian citizens, 87% had a household-adjusted income above 25,000 euros (EUR 1 ≈ Norwegian krone (NOK) 10) per person, 68% were employed/worked, and 8% were students . Two-thi3.2.A total of 13% of the participants reported use of alcohol above the cut-off score for high-risk drinking at T0 according to the AUDIT-C . The lowHigh and severe-risk drinking was most prevalent among the youngest age group (18–29 years) and the least prevalent among the oldest age group (> 70 years). More men than women reported use of alcohol above the cut-off score for high-risk drinking at all measuring points , 5.Men drank almost twice as much as women at baseline [1.57  3.3.A total of 45% of the participants reported moderate PA levels at all measuring points . HoweverWomen had a higher proportion of change between PA levels and measuring points than men see , 4. Part4.Substantial psychological distress was strongly associated with both substantially higher intake of alcohol and lower levels of PA. Male gender, working/studying from home, and having psychological distress were associated with increased alcohol consumption. Fewer METs per week was associated with female gender, being over 70 years old, working/studying from home, and having psychological distress. However, there was a positive time trend with reduced differences in PA levels among those with high and low levels of psychological distress. Inversely, some of the differences with lower levels of alcohol intake among participants with children under 18 years reduced over time. Economic worries were associated with higher PA levels and health worries were associated with lower levels of alcohol consumption at baseline, but no clear time trends. The results of our study showed also that home office/study was strongly associated with higher alcohol consumption and lower PA levels.Prior research has established the relationship between psychological distress and alcohol consumption , 71. A rAn analysis of 11 longitudinal studies showed that the deterioration in mental health during the first lockdown in the UK did not return to baseline levels when social restrictions were eased , hence iThe present study showed that older adults (50–70 years), compared with the ones below 30 years of age, reported higher alcohol consumption. This is consistent with previous general findings of a tendency for high-frequency drinking (5 or more days a week) to increase with age , 79. On It is also worrying that around 20% of men in the present sample display high and severe-risk drinking. The analysis demonstrates that men have a generally high alcohol consumption and a significantly higher one than women, which was sustained throughout the pandemic. Previous research indicates that people with high levels of psychological distress may resort to alcohol as a form of self-medication to cope with or escape from their symptoms , 81; howIncreased alcohol consumption is positively associated with elevated stress and anxiety levels . Stress Being laid off from work could have a negative impact on economic worries. Our results show that participants who were temporarily laid off at baseline did not report an increase in alcohol consumption at the latter measuring point. Compensation packages that the government introduced may have contributed to less economic worry and consequently less emotional/escapism drinking. Layoffs due to COVID-19 have now mostly been resolved.Our findings revealed a significant correlation between working from home and increased alcohol intake as well as decreased PA. There is no significant change observed in either variable over time. Other studies have also shown an association between working from home and increased alcohol consumption and found similar patterns regarding psychological and socio-economic circumstances , 92. TheIn line with other studies , the resIn terms of PA levels, there were some differences based on gender, and differences were found among the age groups. Our findings showed that the oldest participants were generally less active than their younger peers, which is reasonable as they may have wanted to avoid the risk of infection. A recent systematic review showed a reduction in PA levels in the elderly worldwide attributed to the pandemic . At the In addition to the physical restrictions, the mental and emotional toll of the pandemic can also have been a barrier to PA in terms of difficulty in finding the motivation to exercise while struggling with stress, anxiety, and depression. Fear of being infected, mental distress, and a weakened physical capacity because of a COVID-19 infection can explain decreased PA. Other studies identified being fearful of contamination with COVID-19 , and depOverall, the findings suggest that measures to maintain PA during future potential lockdowns should be given priority by individuals, sports organizations, and health authorities, respectively.4.1.The present study had several strengths, including the ability to conduct highly precise and statistically powerful analyses due to the large sample size. Additionally, yearly follow-ups of the participants provided important insight into changes over time during the pandemic era. While a considerable proportion of participants dropped out during the follow-ups, we make adjustments that reduce the likelihood of substantial selection biases, and background factors in each of the groups were similar. On the other hand, the large sample size may result in findings that are statistically significant but not necessarily important and relevant differences. Even though the sample is randomly selected from a wide population, the differences in response between strata of the population may contribute to our cohort not necessarily being completely representative of the source population, with potential limitations in generalizability. Although the recruitment to the study was based on random sampling, the electronic approach could have influenced the results and could have limited people who are less digitally literate.The level of PA was self-reported by participants, hence misclassification of people when using self-reported PA data is a risk  as peoplMoreover, the IPAQ-SF does not measure the intensity of PA but only measures its duration, which does not give an accurate picture of overall PA levels. Further, the IPAQ-SF does not account for different types of PA such as running and walking, which may have different health benefits. Overall, the IPAQ-SF is a valuable tool for measuring PA levels, but it has several inherent limitations that need to be considered . Yet, obWintertime is also viewed as less favorable for outdoor activities  . Hence, Furthermore, the AUDIT-C assesses self-reported information on alcohol consumption during the past 12 months. Thus, recall bias can significantly affect such information, as people’s memories may be inaccurate or incomplete. They might also give greater importance to recent events (recency bias) when providing responses .5.In conclusion, participants with high levels of psychological distress were more likely to have higher alcohol consumption and lower levels of PA at the beginning of the pandemic. However, there was a positive trend to less difference in PA levels over time among those with high levels of psychological distress. Nevertheless, the pandemic may have an amplifying negative effect on those with certain  characteristics.BiE study group: SM, LF, Stine Lehmann, Ragnhild Bjørknes, EH, William Hazell, Øystein Vedaa, JC, Gro Mjeldheim Sandal, Åsgeir Kjetland Rabben and Andreas Roaldsnes.silvia.eiken.alpers@helse-bergen.no.The datasets presented in this article are not readily available because of national regulations on health research. It can be available from the corresponding author on reasonable request. Requests to access the datasets should be directed to SA: The studies involving human participants were reviewed and approved by Regional Committee for Medical and Health Research Ethics, Western Norway. The participants had to tick a box in the questionnaire, to provide informed consent to participate in this study.SM and LF: conceptualization. SM, LF, JS, and SA: methodology. SA, JV, and LF: formal analysis. SA and LF: writing—original draft preparation. L-HL, SP, AM, JV, EH, and JS: writing—review and editing. LF: supervision. LF and SM: project administration. SA, SP, JV, EH, L-HL, JS, AM, SM, and LF contributed to interpretation and writing. All authors contributed to the article and approved the submitted version.This project was co-founded by the University of Bergen and Bergen municipality.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.https://www.frontiersin.org/articles/10.3389/fpsyt.2023.1181046/full#supplementary-materialThe Supplementary material for this article can be found online at: Click here for additional data file.