A preponderance of empirical research in higher education exists on the use of electronic resources to promote university education and learning. This suggests that this area of research has attracted significant interest worldwide. However, there seems to be inadequate information on the association between specific electronic infrastructures, how they are utilized for learning, and their effects on students’ learning effectiveness in higher institutions in Nigeria. This research draws on previous studies and seeks to establish how different electronic resources are used (...) in universities and how they impact students’ learning effectiveness. Using a descriptive survey design, this survey examined 1,452 participants (undergraduates and postgraduates) from Nigerian universities. The data were analyzed using mean and standard deviation, while a Polytomous Logistic Regression Model was used in testing the hypothesis formulated. Findings showed that the degree of the use of digital infrastructures in universities is low. The extent of learning effectiveness among students in universities is also low. The usage of e-infrastructures (such as e-learning, e-communication, and e-library tools) significantly predicted students’ levels of learning effectiveness. Based on these results, conclusions and relevant theoretical and practical implications are discussed for policy reforms in education and e-learning. This study faces a limitation arising from the small number of universities studied. However, it is suggested that future related studies extend their focus beyond the context of Nigeria. (shrink)

The University of Tehran English Proficiency Test (UTEPT) is a high-stakes entrance examination taken by more than 10,000 master’s degree holders annually. The examinees’ scores have a significant influence on the final decisions concerning admission to the University of Tehran Ph.D. programs. As a test validation investigation, the present study, which is a bias detection research in nature, utilized multistep logistic regression (LR) procedure to examine the presence of gender differential item functioning (DIF) in the UTEPT with a (...) sample of 6,555 examinees who took the test in November 2006. Specifically, the LR DIF two-degree of freedom Chi-squared test of significance was employed to test the significance of DIF. Following what has been recently suggested in the literature, the test of significance for DIF was accompanied by a measure of magnitude, namely the R-squared effect size for LR DIF, for the interpretation of which the two widely accepted classification schemes were used comparatively. The results reveal that 39 of the 100 items in the test display significant gender differences. However, these group differences are viewed as “negligible” based on both of the schemes. Accordingly, it could be argued that the UTEPT is a gender DIF-free test, though the Reading Comprehension section of the test remains in need of further analysis as it seems that the general trend of DIF indices at the item level may hint at an inclination towards males. (shrink)

Every year, the Vietnamese people reportedly burned about 50,000 tons of joss papers, which took the form of not only bank notes, but iPhones, cars, clothes, even housekeepers, in hope of pleasing the dead. The practice was mistakenly attributed to traditional Buddhist teachings but originated in fact from China, which most Vietnamese were not aware of. In other aspects of life, there were many similar examples of Vietnamese so ready and comfortable with adding new norms, values, and beliefs, even contradictory (...) ones, to their culture. This phenomenon, dubbed “cultural additivity”, prompted us to study the co-existence, interaction, and influences among core values and norms of the Three Teachings –Confucianism, Buddhism, and Taoism–as shown through Vietnamese folktales. By applying Bayesian logistic regression, we evaluated the possibility of whether the key message of a story was dominated by a religion (dependent variables), as affected by the appearance of values and anti-values pertaining to the Three Teachings in the story (independent variables). Our main findings included the existence of the cultural additivity of Confucian and Taoist values. More specifically, empirical results showed that the interaction or addition of the values of Taoism and Confucianism in folktales together helped predict whether the key message of a story was about Confucianism, β{VT ⋅ VC} = 0.86. Meanwhile, there was no such statistical tendency for Buddhism. The results lead to a number of important implications. First, this showed the dominance of Confucianism because the fact that Confucian and Taoist values appeared together in a story led to the story’s key message dominated by Confucianism. Thus, it presented the evidence of Confucian dominance and against liberal interpretations of the concept of the Common Roots of Three Religions (“tam giáo đồng nguyên”) as religious unification or unicity. Second, the concept of “cultural additivity” could help explain many interesting socio-cultural phenomena, namely the absence of religious intolerance and extremism in the Vietnamese society, outrageous cases of sophistry in education, the low productivity in creative endeavors like science and technology, the misleading branding strategy in business. We are aware that our results are only preliminary and more studies, both theoretical and empirical, must be carried out to give a full account of the explanatory reach of “cultural additivity”. (shrink)

Ranking theory is a formal epistemology that has been developed in over 600 pages in Spohn's recent book The Laws of Belief, which aims to provide a normative account of the dynamics of beliefs that presents an alternative to current probabilistic approaches. It has long been received in the AI community, but it has not yet found application in experimental psychology. The purpose of this paper is to derive clear, quantitative predictions by exploiting a parallel between ranking theory and a (...) statistical model called logistic regression. This approach is illustrated by the development of a model for the conditional inference task using Spohn's ranking theoretic approach to conditionals. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. (...) The dataset was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

Sleep onset is associated with marked changes in behavioral, physiological, and subjective phenomena. In daily life though subjective experience is the main criterion in terms of which we identify it. But very few studies have focused on these experiences. This study seeks to identify the subjective variables that reflect sleep onset. Twenty young subjects took an afternoon nap in the laboratory while polysomnographic recordings were made. They were awakened four times in order to assess subjective experiences that correlate with the (...) (1) appearance of slow eye movement, (2) initiation of stage 1 sleep, (3) initiation of stage 2 sleep, and (4) 5 min after the start of stage 2 sleep. A logistic regression identified control over and logic of thought as the two variables that predict the perception of having fallen asleep. For sleep perception, these two variables accurately classified 91.7% of the cases; for the waking state, 84.1%. (shrink)

Purpose ‐ The purpose of this paper is to explore the "resource curse" problem as a counter-example of creative performance and innovation by examining reliance on capital and physical resources, showing the gap between expectations and ex-post actual performance that became clearer under conditions of economic turmoil. Design/methodology/approach ‐ The analysis uses logistic regressions with dichotomous response and predictor variables on structured tables of count data, representing firm performance as an outcome of capital resources, physical resources and innovation where (...) appropriate. Findings ‐ Key findings relevant to economic and business practice follow. First, a typical characteristic of successful Vietnamese firms in the transition period is their reliance on either capital resources or physical asset endowments. Second, poor performers exhibit evidence of over-reliance on both capital and physical assets. Third, firms that relied on both types of resources tended to downplay creative performance. Some evidence suggests that firms face more acute problem caused by the law of diminishing returns in troubled times. Fourth, the "innovation factor" has not been tapped as a source of economic growth. Research limitations/implications ‐ This study has some limitations. The size of the survey sample is approximately 150 firms, while the potential sample of > 300 should be possible in the future. When the size increases, the research could be expanded to include further variables that will help investigate more deeply into the related issues and business implications. With regard to the implications of the study, the absence of innovations has made the notion of "resource curse" identical to "destructive creation" implemented by ex-ante resource-rich firms, and worsened the problem of resource misallocation in transition turmoil. The Vietnamese corporate sector's addiction to resources may contribute to economic deterioration, through a downward spiral of lower efficiency leading to consumption of more resources. Practical implications ‐ Insights obtained from this study could save transition economies' resources which have almost always been considered sine qua non before any critical major policymaking, while this is not necessarily true, and in many cases, even counterproductive. Originality/value ‐ Original data set on Vietnam stock market are collected, processed, prepared and used by the authors. Original design by the authors for regression equations with dichotomous predictor variables: dependence on endowed physical assets, reliance on capital resources and significant signs of creative performance/innovations. Original idea of viewing "resource curse" as absence of innovation and due to uncreative "destructive creation" of poor-performing commercial operations by resource-rich firms is used in the paper. We have searched the literature in business research and found that the empirical results have not been previously reported. (shrink)

We examined the links between moral identity—the centrality of moral principles to identity—and political purpose during emerging adulthood. We analyzed data from two waves of a longitudinal study of civic purpose. T1 surveys were collected before high school graduation and T2 survey were collected two years later. We categorized people (N = 1,578 at T1 and N = 480 at T2) into political purpose groups based on the person-centered perspective and then performed multinomial logistic regression analysis to test (...) whether moral identity was associated with categories of political purpose. The findings from our study indicate that moral identity at T1 is linked with the maintenance and formation of T2 political purpose. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Religion has always been argued as a cultural factor that influences behavior. Similarly, previous studies had shown that demographic factors such as age, gender, education level, and work experience were linked to behavior. This present study examined if demographic factors and religiosity dimensions could predict ethical behavior, specifically Islamic ethical work behavior, in Brunei. This study used an existing data set, which had been employed with a different approach and research questions. A total of 370 respondents were analyzed. Model analysis (...) involved the use of binomial logistic regression. It correctly classified 77% of cases. The result showed that demographic factors were not significant predictors of Islamic ethical work behavior. However, all religiosity dimensions were significant predictors of Islamic ethical work behavior, providing support for religion influencing behavior in the workplace—to a certain extent, in the society—although the degree of effects varied, suggesting opportunities for further research. The findings were discussed, and limitations and recommendations were then presented. (shrink)

Political fact-checkers evaluate the truthfulness of politicians’ claims. This paper contributes to an emerging scholarly debate on whether fact-checkers treat political parties differently in a systematic manner depending on their ideology (bias). We first examine the available approaches to analyze bias and then present a new approach in two steps. First, we propose a logistic regression model to analyze the outcomes of fact-checks and calculate how likely each political party will obtain a truth score. We test our model (...) with a sample of fact-checks from Newtral, a major Spanish fact-checker. Our model would signal bias under two assumptions: a) all political parties are on average equally accurate in their statements; b) the verification method gives precise instructions and is implemented systematically. We investigate this second assumption with a series of interviews with Newtral fact-checkers. We show that standard verification protocols are so loosely implemented that fact-checks reflect a set of journalistic decisions, rather than a bias in the statistical sense. We call for a more rigorous definition of verification methods as a pre-requisite for an unbiased assessment of politician’s claims. (shrink)

Mexico, with the commissioning of the "National Crusade Against Hunger Program" in 2013, aimed at serving the population that presents both extreme poverty and food deprivation. The article aims to analyze whether the criterion of the selection of the municipalities of the State of Veracruz incorporated in the National Crusade Against Hunger Program (PNCH) show complementarity with the efforts in the fight against poverty in the social expenditure strategy applied in the Priority Attention Zones Program (ZAP) and the Priority Areas (...) Development Program (PDZP) and, particularly, the indigenous municipalities that have a greater degree of social exclusion. The adjustment of a binary logistic regression model is presented, in order to assess the incidence of contextual factors to interpret the scope of the strategy adopted by the federal government in the fight against poverty and hunger. As a result, it is evident that there is no continuity in the fight against poverty, since the municipalities included in the strategy Priority Areas of Attention and Program of Development of Priority Zones are not considered in the selection of municipalities incorporated in the National Program of Crusade Against Hunger, a situation that identifies the relationship between programs is not complementary. (shrink)

Presently, there is a lack of psychological and quantitative studies in Singapore about discriminatory attitudes. This paper aimed to contribute to this aspect. However, to examine actual behavior can be difficult due to the sensitive nature of the needed data. Hence, this study approached discrimination at an attitudinal level. Six vulnerable groups were examined in this study. They consisted of people of a different race, immigrants or foreign workers, homosexuals, people living with HIV/AIDS, people of a different religion, and unmarried (...) couples living together. Two research questions were posed: 1) What is the prevalence of having discriminatory attitude toward vulnerable groups? and 2) What are the predictors of these discriminatory attitudes? Using a sample population of 1,972 Singaporeans, descriptive analysis and binomial logistic regression analysis were conducted. Firstly, based on the results, the prevalence ranged between 10.76% to 42.46%. Singaporeans have discriminatory attitude toward vulnerable groups who can be categorized into two: the least discriminated (three groups ranging between 10.76% to 15.48%) and the highly discriminated (three groups ranging between 30.86% to 42.46%). Secondly, binomial logistic regression showed support for several significant predictors such as emphasis on the importance of religion and tradition, and employment status, depending on the model assessed. However, one pattern was observed in all the models, that a person who discriminates one group is more likely to discriminate another group. The findings were then discussed and explained within the context of Singapore. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Aim: Low professional help-seeking intention (PHSI) hinders effective treatment of mental illness. PHSI among Chinese students is still understudied and under-recognized. This study aimed to evaluate the status of PHSI and its associated risk factors among Chinese medical students. -/- Methods: A cross-sectional survey was conducted in Hainan province, South China, between January 1, 2021, and May 31, 2021. A total of 2182 medical students were recruited and surveyed via an anonymous structured questionnaire. Logistic regression analyses were performed (...) to identify the factors associated with PHSI. -/- Results: Among the 2182 medical students (mean age 21.0 years (SD = 3.70), 61.5% females), those with and without PHSI were 72.0% and 28.0%, and 16.4% with moderate to severe depression. Male students, those with a high level of depression stigma, serious family dysfunction, and heavy dependence on mobile phones were significantly less likely to seek professional mental health help, with odds ratios (ORs) of 1.5, 2.0, 2.1, and 1.7, respectively. -/- Conclusion: A significant proportion of Chinese medical students demonstrate low PHSI, influenced by factors such as gender, depression stigma, family dysfunction, and mobile phone dependence. Future interventions aimed at increasing medical students’ PHSI should prioritize reducing depression stigma, mitigating reliance on mobile phone use, and enhancing family function to address these key barriers to seeking professional mental health support. (shrink)

Background: The most common hematological abnormality among people infected with Human Immunodeficiency Virus (HIV) is anemia. This is also related to high mortality risk among patients receiving Antiretroviral Therapy (ART). Purpose: This study aimed to identify predictors of anemia among HIV patients taking ART using a regimen of the single-tablet drug contain tenofovir, lamivudine, and efavirenz in Jayapura, Papua. Methods: This was a cross-sectional study conducted at Jayapura regional hospital from June to September 2017. A total of 80 HIV patients (...) were eligible for analysis. Data collection was conducted through an interview, medical record, measurement, and laboratory assay. The significance of differences among categorical variables was analyzed using Fisher’s exact test. The variables with p < 0.25 were then analyzed using binary logistic regression to determine predictors associated with anemia. A threshold of p < 0.05 indicates statistical significance. Results: This study showed that Body Mass Index (BMI) <18.50 (p = 0.01; OR = 5.63; 95% CI = 1.43 < OR < 22.19), length on ART ≤12 months (p = 0.00; OR = 4.90; 95% CI = 1.65 < OR < 14.53), and Cluster of Differentiation 4 (CD4+) percentage out of normal (p = 0.02; OR = 0.19; 95% CI = 0.05 < OR <0.77) had a significant association with anemia. Conclusion: BMI, length on ART, and CD4+ percentage were predictors of anemia among HIV patients taking antiretroviral therapy containing tenofovir, lamivudine, and efavirenz regimen in Jayapura. (shrink)

Appropriate measurement scales are fundamental in data analysis, allowing researchers to categorise, select appropriate statistical methods, and analyse and interpret their data accurately. The nominal scale is one such measurement scale in behavioural sciences, which is crucial in organising data into distinct categories. This paper provides an overview of the nominal measurement scale in research data analysis. It explains the characteristics and role of the nominal scale in organising data into distinct categories. The paper discusses methods of collecting nominal scale (...) data, including surveys and observations. It explores the use of the nominal scale in descriptive (such as frequency counts, measures of dispersion and central tendencies), and inferential statistics (such as point biserial correlation, independent t-test, analysis of variance, logistic regression, discriminant analysis, differential item functioning, chi-square test of independence, Kruskal-Walli’s test, and Mann-Whitney U Test). Each technique is explained with assumptions and application areas. In conclusion, the paper emphasises the significance of the nominal scale in data analysis and its contribution to various statistical techniques. It serves as a comprehensive guide for researchers and practitioners looking to understand and utilise the nominal measurement scale in their data analysis. (shrink)

As moral injury is a still-emerging concept within the area of military mental health, prevalence estimates for moral injury and its precursor, potentially morally injurious events (PMIEs), remain unknown for many of the world’s militaries. The present study sought to estimate the prevalence of PMIEs in the Canadian Armed Forces (CAF), using data collected from CAF personnel deployed to Afghanistan, via logistic regressions controlling for relevant sociodemographic, military, and deployment characteristics. Analyses revealed that over 65% of CAF members reported (...) exposure to at least one event that would be considered a PMIE. The most commonly PMIEs individuals reported included seeing ill or injured women and children they were unable to help (48.4%), being unable to distinguish between combatants and non-combatants (43.6%), and finding themselves in a threatening situation where they were unable to respond due to the rules of engagement under which they were required to operate (35.4%). These findings provide support for both the presence of exposure to PMIEs in CAF members and the need for formal longitudinal data collection regarding PMIE exposure and moral injury development. (shrink)

The study investigated the awareness and utilization of e-learning technologies among vocational and technical education (VTE) students. A descriptive survey research design was used for this study, and the population consisted of 551 VTE students. A simple random sample of 62 VTE students was selected. The data collection instrument was a questionnaire. The reliability of the questionnaire was assessed using Cronbach's alpha analysis, and the reliability index was found to be within the acceptable limit of 0.70. An online survey using (...) Google Forms was used to collect the data. The study tested two null hypotheses using binary logistic regression models based on age, department level of study, and devices owned. The data was analyzed using frequency, percentages, and mean. The major findings from the data analysis indicated that VTE students had an awareness and utilization rate of 84.8% for the e-learning website, 75.6% for Google Classroom, and 60.6% for smart boards and video conferencing. However, 24.4% of the students were still unaware of the Moodle Learning Management System and the Canvas e-learning platform at the University of Nigeria Nsukka. Based on these findings, it is recommended that VTE students receive proper training to effectively utilize the e-learning technologies available to them, which would aid in fostering creativity and innovation. (shrink)

Background and objectives: Bangladesh has the highest malnutrition rates in the world. The main objective of this study is to identify and determine the main factors for child malnutrition among children under the age 5 years in rural Bangladesh. -/- Material and methods: This research was conducted in rural areas at Meherpur district in Bangladesh. Convenience sampling method is used and 85 under 5 child information is collected through a structured questionnaire. The information’s of child’s are gathered from the mothers (...) of the child. Malnourished children is measured by using APLS method and WHO Child Growth Standard Median Index. Binary logistic regression model is used to find out the key factors of malnutrition. -/- Results: It is found that family income, maintain proper diet during pregnancy period, proper diet maintain for children have negative significant (p<0.05) effect on child malnutrition. -/- Conclusion: The magnitude of the child’s malnutrition still is of great concern in Bangladesh. Since, poverty, imbalanced diet during pregnancy period and imbalanced diet for under 5 children influences to malnutrition are significantly associated with high prevalence of malnutrition, so government and people should reduce these factor to reduce malnutrition in rural area of Bangladesh. (shrink)

The study is based on primary data which are collected with the help of a structured schedule from four different castes of population spread over North Tripura District and Unokuti District of Tripura. Stratified random sampling technique was applied for the collection of data at household level where the sample units are the adult women in the age group of 18 - 55 years. All the data are collected based on the criteria of balanced representation of different social castes such (...) as ST, SC, OBC and General in the sample. Altogether 90 samples have been collected. Main objective of the study is to determine the morbidity status and also to determine the key variables which affect morbidity status of the people of North Tripura. Binary logistic regression model has been applied to determine the key determinants of morbidity status. It has been found that age of the respondent, respondents per day calorie intake and educational level of the respondents are the key determinants of morbidity status of the respondents. The model as a whole explained between 30.6% (Cox & Snell R Square) and 41.1 %( Nagelkerke R Square). And ᵡ2 (6.460, N=90) =32.811. (shrink)

Education for women in Muslim societies had been discussed widely. However, it remains unclear if the perception of the importance of university education in Muslim societies and its predictors are different between men and women. Therefore, this research examined the following misogynistic perception among both genders: university education is more important for men than for women. This research aimed to determine gender differences and predictors of this perception. Sample populations were from Malaysia (N=820), Singapore (N=320), India (N=447), and Pakistan (N=1195). (...) Mean difference test and binomial logistic regression analysis were conducted. The results revealed that more than half of the men agreed that they perceived university education as more important for men than for women. Over half of the women had the same perception. Some demographic variables were significant predictors of perceiving university education to be more important for men than for women, albeit inconsistent based on groups and countries. Some of the findings included being religious predicted less likelihood of misogynistic perception in countries such as Malaysia and Pakistan, but not in the others. Consistently, for both genders, perceptions concerning men’s superiority and women’s role being at home predicted the perception that university education was more important for men than for women. This suggests the intricate influence of the perception of ability and gender stereotype among Muslims when it concerns university education. Further studies are necessary to understand the rationale behind these predictors. (shrink)

BACKGROUND Preconception sex selection for non-medical reasons is a controversial issue in bioethics. Little research has described preferences for preconception sex selection among Arab populations. This study describes the sex preference and interest in employing sex selection techniques among pregnant women in northern Jordan. -/- METHODS A self-reported questionnaire was administered to 600 pregnant women in Irbid, Jordan. χ2 test and binary logistic regression were used to examine the factors associated with interest in preconception sex selection. -/- RESULTS (...) In general, the interest in using sex selection was low. Women who preferred boys were more likely to be interested in sex selection, if paid for by the couple [odds ratio (OR) = 4.40, 95% confidence interval (CI): 1.75–11.11] or by health insurance (OR = 3.42, 95% CI: 1.94–6.06), or, if feasible, administered through oral medication (OR = 8.84, 95% CI: 5.05–15.63). Women with lower education were more likely to be interested in sex selection, if paid by health insurance (OR = 1.96, 95% CI: 1.10–3.45) and were more likely to believe that sex selection is legal (OR = 1.79, 95% CI: 1.06–2.86). Women who had no boys were more likely to be interested in sex selection, if paid by health insurance (OR = 1.94, 95% CI: 1.10–3.42) or, if feasible, through medication (OR = 3.03, 95% CI: 1.82–5.00). -/- CONCLUSIONS The majority of participants were not in favor of using preconception sex selection. Those with a preference to have boys, with lower education, and those with an imbalanced family were more likely to be interested in using sex selection technology. (shrink)

Background: The time before surgery is a traumatic period for patients. Despite this fact, no research has been conducted on nurses’ preoperative patient education in Ethiopia. This study aimed to assess preoperative patient education practices and associated factors among nurses working in East Amhara comprehensive specialized hospitals, Ethiopia, 2022. Methods: A hospital-based cross-sectional study was conducted with 416 nurses. Pretested, structured questionnaires were used to collect the data. Bivariable analysis was performed for each independent variable with a P-value < 0.25 (...) on the data imported to multivariate logistic regression analysis. AOR with a 95% CI and a P-value < 0.05 at a 5% level of significance were considered. Results: Only 38.5% of nurses were found to have good practices for preoperative patient education, with a response rate of 98%. Nurses with 6 years of work experience (AOR = 3.15, 95% CI: 1.692– 5.874), adequate time (AOR = 2.33, 95% CI: 1.119– 4.889), training (AOR = 4.27, 95% CI: 1.548– 11.796), age 25– 29 (AOR = 0.15, 95% CI: 0.070– 0.331), age 30– 34 (AOR = 0.25, 95% CI: 0.137– 0.479), and knowledge (AOR = 3.73, 95% CI: 2.222– 6.273) were significantly associated. Conclusion: Preoperative patient education practices among nurses were poor. Work experience, knowledge, training, and adequate time were found to be significant. Organize preoperative patient education programs for nurses that share experiences and provide ongoing training. (shrink)

Introduction: There is no study investigating the factors predicting postoperative febrile urinary tract infection (fUTI) in pediatric patients. We aimed to determine the factors predicting postoperative fUTI in pediatric patients who underwent ureterorenoscopic lithotripsy (URS-L). -/- Methods: Pediatric patients who underwent URS-L due to ureter or kidney stones in our clinic between 2012 and 2019 were analyzed retrospectively. The demographic data, stone characteristics, intraoperative and postoperative data of those with and without postoperative fUTI were compared. Univariable and multivariable binary (...) class='Hi'>logistic regression analysis was performed to determine predictors of fUTI. -/- Results: A total of 136 patients were included in the study. Postoperative fUTI developed in 17 patients (12.5%). History of stone surgery, history of UTI, presence of nephrostomy/D-J stent, type of surgery, operation time, length of stay, and presence of complication except fUTI were found to be significantly different in the fUTI group. In the univariate analysis, history of stone surgery, history of UTI, presence of nephrostomy/D-J stent, type of surgery, operation time, and presence of complication except fUTI were determined as factors predicting fUTI. In multivariate analysis, only the history of UTI was identified as an independent predictive factor. -/- Conclusion: In pediatric patients, infectious complications constitute the majority of complications after URS-L. History of UTI is the only independent factor that predicts postoperative fUTI after URS-L. (shrink)

Although an inverse relationship between religion and deviance is empirically well-established in the western context, previous studies on Islam and deviance conducted in non-western countries are limited. To address this gap in deviance research, we hypothesized that individual religiosity would be inversely related to deviance with the inverse relationship being more likely for ascetic than anti-ascetic or secular deviance. To test this hypothesis, we applied ordinary least squares and logistic regression methods to analyze data collected from 2,005 survey (...) participants of a Turkish public university student population. Regression results provided partial support for the hypothesis, as we found that religiosity was inversely related to both ascetic and secular deviance. The observed inverse relationship is noteworthy in that it was found in an institutionally secular, Muslim country. Implications of our findings are discussed. (shrink)

Coronary Heart Disease (CHD) is one of the leading causes of death nowadays. Prediction of the disease at an early stage is crucial for many health care providers to protect their patients and save lives and costly hospitalization resources. The use of machine learning in the prediction of serious disease events using routine medical records has been successful in recent years. In this paper, a comparative analysis of different machine learning techniques that can accurately predict the occurrence of CHD events (...) from clinical data was performed. Four machine learning classifiers, namely Logistic Regression, Support Vector Machine (SVM), K- Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) Neural Networks were identified and applied to a dataset of 462 medical instances and 9 features as well as the class feature from the South African Heart Disease data retrieved from the KEEL repository. The dataset consists of 302 records of healthy patients and 160 records of patients who suffer from CHD. In order to handle the imbalanced classification problem, the K-means algorithm along with Synthetic Minority Oversampling TEchnique (SMOTE) was used in this study. The empirical results of applying the four machine learning classifiers on the oversampled dataset have been very promising. The results reported using different evaluation metrics showed that SVM has achieved the highest overall prediction performance. (shrink)

Abstract—Breast cancer is considered the second most common cancer in women compared to all other cancers. It is fatal in less than half of all cases and is the main cause of mortality in women. It accounts for 16% of all cancer mortalities worldwide. Early diagnosis of breast cancer increases the chance of recovery. Data mining techniques can be utilized in the early diagnosis of breast cancer. In this paper, an academic experimental breast cancer dataset is used to perform a (...) data mining practical experiment using the Waikato Environment for Knowledge Analysis (WEKA) tool. The WEKA Java application represents a rich resource for conducting performance metrics during the execution of experiments. Pre-processing and feature extraction are used to optimize the data. The classification process used in this study was summarized through thirteen experiments. Additionally, 10 experiments using various different classification algorithms were conducted. The introduced algorithms were: Naïve Bayes, Logistic Regression, Lazy IBK (Instance-Bases learning with parameter K), Lazy Kstar, Lazy Locally Weighted Learner, Rules ZeroR, Decision Stump, Decision Trees J48, Random Forest and Random Trees. The process of producing a predictive model was automated with the use of classification accuracy. Further, several experiments on classification of Wisconsin Diagnostic Breast Cancer and Wisconsin Breast Cancer, were conducted to compare the success rates of the different methods. Results conclude that Lazy IBK classifier k-NN can achieve 98% accuracy among other classifiers. The main advantages of the study were the compactness of using 13 different data mining models and 10 different performance measurements, and plotting figures of classifications errors. (shrink)

This study investigated changes in the prevalence of depression, stress, and suicidal attempts during the COVID-19 pandemic. The ≥19-year-old population in the Korea National Health and Nutrition Examination Survey in 2019 and 2020 was included. The histories of depression, stress, and suicidal attempts were compared between the 2019 and 2020 cohorts using multiple logistic regression analysis with complex sampling. The prevalence of depression was not significantly different between the 2019 and 2020 groups (4.1% vs. 4.5%, p = 0.326). (...) The prevalence of stress and suicide attempts was also not significantly different between groups (all p > 0.05). The rates of depression, stress, and suicide attempts were not associated with the 2020 group compared to the 2019 group (all p > 0.05). The 19- to 39-year-old group in the 2020 group indicated a higher rate of depression (diagnosed by physicians) than the 19- to 39-year-old group in the 2019 group (adjusted odds ratio = 1.58, 95% confidence intervals = 1.00–2.50, p = 0.049). The risks of depression, stress, and suicidal attempts were not related to the COVID-19 pandemic in Korean adults. A young adult population demonstrated an increased risk of depression associated with the COVID-19 pandemic. (shrink)

Background: The World Health Organization has endorsed a community-based health insurance scheme (CBHIS) as a shared financing plan to improve access to health services and ensure universal coverage of the healthcare delivery system. Such a contributory scheme is the most likely option to provide health insurance coverage when governments cannot offer direct health care support. Despite improvements in access to current healthcare services, Ethiopia’s healthcare delivery remained low, owing to the country’s underdeveloped healthcare finance system. As a result, the present (...) study assessed CBHIS coverage and its predictors in Ethiopia at the individual and community level. Methods: The availability of CBHIS was checked via a criterion: at least one of the cluster respondents had to be enrolled in CBHIS. This study was based on secondary data from the Ethiopia Mini Demography and Health Survey (EMDHS) 2019 and included 7724 respondents. The study population was described using percentage and frequency. Four multilevel mixed-effects logistic regression modelling stages were performed to control for variations due to heterogeneity across clusters, and determinant predictors of CBHIS enrollment were outplayed. Results: The prevalence of CBHIS enrollment in Ethiopia was 33.13%. Rural residents were 3.218 times (AOR = 3.218; 95% CI: 1.521, 6.809), male household heads were 1.574 times (AOR = 1.574, 95% CI: 1.105, 2.241), getting funds from the safety net program were times 2.062 (AOR = 2.062, 95% CI: 1.297, 3.279), attending the primary educational level was 1.686 times (AOR = 1.686, 95% CI: 1.007, 2.821), bank accounts were 1.373 times (AOR = 1.373, 95% CI: 1.052, 1.792), and wealth index was 1.356 times (AOR = 1.356, 95% CI: 1.001, 1.838) more likely associated with CBHIS coverage, whereas the regions, the other religions, and women aged 20– 24 had lower odds of CBHIS coverage. Conclusion: In Ethiopia, regional healthcare expenditure per capital, religious affiliation, women age range, residents, sex of household head, funds from the safety net program, formal educational level, and having bank accounts were associated with community-based health insurance scheme coverage. (shrink)

Abstract: Background: Coronary artery disease remains the leading cause of morbidity and mortality Worldwide. Previous reviews pointed that nursing interventions are beneficial for coronary artery patients. However, most interventions focused on education and counselling, but not consistent with the outcome set; still did not consider patient’s coronary artery disease risky characteristics. Related studies in China also difficult to find. Therefore this study was conducted to investigate kinds of nursing interventions delivered to coronary artery patients and match them with patient’s risk (...) factors of coronary artery disease. Results of this study were expected to add new knowledge that will alert nurses to consider coronary artery risk factors which in turn might enable the development of appropriate approaches to improve patient’s wellbeing hence reduce frequent coronary artery morbidity and mortality. Methods: A descriptive, cross-sectional, retrospective design using clinical case notes was employed. Study was undertaken in coronary care wards at the teaching hospital in China from November 2017 to September 2018. Structured-literature supported self-designed questionnaire was utilized for data collection. Chi square (χ2) test and multivariate logistic regression for adjusted odds ratio with 95% confidence interval were used to compare the relationship among independent (patient’s risk coronary artery disease factors) and dependent (nursing interventions) categorical variables. Ethical permission was granted accordingly. Results: A total of 300 coronary artery patients’ case notes were audited with mean age 63±11.2 years. Of these 175 (58.3%) were males. 126(42%) were smoking and 224(74.7%) were hypertensive. More evidence based nursing interventions than education and counselling were found to be delivered to these patients. “Administer coronary artery disease medication and their instructions” was mostly delivered to many patients 291(97%) while “counsel to cope with stress” was the least one 60 (20.0%). Three of eight nursing interventions delivered significantly matched with three or all of these patient’s coronary artery risk variables (age, smoking, hypertension and diabetes) (p < 0.05 and/or < 0.01) with Adjusted odds ratio (95% CI) within their significant ranges. Conclusion: This study delivers valuable insight that, nurses in the studied teaching hospital delivered beneficial evidence based nursing interventions to patients with coronary artery disease which significantly matched with their risk factors of coronary artery illness. However, care for stress was low hence needs improvement. Furthermore, research is needed to get consistency of nursing interventions with patient’s end point clinical outcomes for further appraisal of nursing efforts in caring CAD patients . (shrink)

Consumers are increasingly able to easily access health information online about food products. However, consumers have difficulty identifying reliable health information from diverse sources along with information about the coronavirus disease (COVID-19) pandemic because the inundation of information (both true and false) overwhelm consumers. We investigated the usefulness of a checklist confirmation scheme for identifying unreliable COVID-19-related health information. Data were collected from June 30–July 1, 2021. First, we measured 700 participants’ baseline health literacy levels by having them read unreliable (...) health information about the efficacy of green tea intake in preventing COVID-19 based on the results of animal experimentation. Second, participants read an explanation with a five-step flowchart of how to identify reliable health information. Thereafter, we remeasured participants’ health literacy levels. To identify the factors hindering the effect of the confirmation scheme, a logistic regression analysis was performed to calculate odds ratios (ORs) and 95% confidence intervals (CIs). Overall, 77.9% (293/376) of those with low health literacy levels at baseline still had low literacy after the intervention. The factor that hindered the confirmation scheme’s usefulness was benefit perceptions of food ingredients (OR: 0.493; 95% CI: 0.252–0.966). Consumers with higher benefit perceptions of a target product faced more difficulties using the confirmation scheme effectively. Therefore, the most effective strategies involve filtering information at the organizational level rather than the individual level, which should help consumers correctly identify misinformation concerning food and health and promote accurate decision-making. (shrink)

: COVID-19 has proliferated over the earth, exposing mankind at risk. The assets of the world's most powerful economies are at stake due to the disease's high infectivity and contagiousness. The capacity of machine learning algorithms can estimate the amount of future COVID-19 cases, which is now considered a possible threat to civilization. Five conventional measuring models, notably LR, LASSO, SVM, ES, and LSTM, were utilised in this work to examine COVID-19's undermining variables. Each model contains three sorts of expectations: (...) the number of newly contaminated cases, the number of passings, and the number of recoveries. However, it is hard to anticipate the patients' real outcomes. To address the issue, a suggested approach based on long transient memory (LSTM) forecasts the number of COVID-19 cases in the next 10 days as well as the impact of preventative measures such as social isolation and lockdown on COVID-19 spread. (shrink)

Regress arguments have convinced many that reasoning cannot require beliefs about what follows from what. In this paper I argue that this is a mistake. Regress arguments rest on dubious (although deeply entrenched) assumptions about the nature of reasoning — most prominently, the assumption that believing p by reasoning is simply a matter of having a belief in p with the right causal ancestry. I propose an alternative account, according to which beliefs about what follows from what play a constitutive (...) role in reasoning. (shrink)

How should you decide what to do when you’re uncertain about basic normative principles? A natural suggestion is to follow some "second-order:" norm: e.g., obey the most probable norm or maximize expected choiceworthiness. But what if you’re uncertain about second-order norms too—must you then invoke some third-order norm? If so, any norm-guided response to normative uncertainty appears doomed to a vicious regress. This paper aims to rescue second-order norms from the threat of regress. I first elaborate and defend the claim (...) some philosophers have made that the regress problem forces us to accept normative externalism, the view that at least one norm is incumbent on all agents regardless of their normative beliefs. But, I then argue, we need not accept externalism about first-order norms, thus closing off any question of how agents should respond to normative uncertainty. Rather, we can head off the threat of regress by ascribing external force to a single second-order norm: the enkratic principle. (shrink)

Recent work by Peijnenburg, Atkinson, and Herzberg suggests that infinitists who accept a probabilistic construal of justification can overcome significant challenges to their position by attending to mathematical treatments of infinite probabilistic regresses. In this essay, it is argued that care must be taken when assessing the significance of these formal results. Though valuable lessons can be drawn from these mathematical exercises (many of which are not disputed here), the essay argues that it is entirely unclear that the form of (...) infinitism that results meets a basic requirement: namely, providing an account of infinite chains of propositions qua reasons made available to agents. (shrink)

Plausibly, no agent ever performs an action without some desire to perform that action. If so, a regress argument shows that, given incompatibilism, we are only rarely free. The argument sidesteps recent objections to this thesis.

The paper aims to elucidate in better detail than before the dispute about whether or not dispositional monism—the view that all basic properties are pure powers—entails a vicious infinite regress. Particular focus is on Alexander Bird's and George Molnar's attempts to show that the arguments professing to demonstrate a vicious regress are inconclusive because they presuppose what they aim to prove, notably that powers are for their nature dependent on something else. I argue that Bird and Molnar are mistaken. It (...) is true that dispositional monism is popularly assumed to characterize powers as dependent entities, but this is not what the arguments aim to prove. They merely aim to demonstrate that it would be absurd to assume that all properties are dependent in this way. Finally, it is argued that there is an unresolved tension in Bird's and Molnar's accounts of powers. They characterize them as being for their nature dependent on the manifestations that they are for, and yet ontologically independent of those same manifestations. Until that tension is resolved, their accounts are not equipped to remove the threat of vicious regress. (shrink)

The Language of Thought Hypothesis is often taken to have the fatal flaw that it generates an explanatory regress. The language of thought is invoked to explain certain features of natural language (e.g., that it is learned, understood, and is meaningful), but, according to the regress argument, the language of thought itself has these same features and hence no explanatory progress has been made. We argue that such arguments rely on the tacit assumption that the entire motivation for the language (...) of thought consists in explaining the explanandum that allegedly generates the regress. But this tacit assumption is simply false. The Language of Thought Hypothesis is a cogent view and one with considerable explanatory advantages. (shrink)

In this work we propose an encoding of Reiter’s Situation Calculus solution to the frame problem into the framework of a simple multimodal logic of actions. In particular we present the modal counterpart of the regression technique. This gives us a theorem proving method for a relevant fragment of our modal logic.

This paper uses a schema for infinite regress arguments to provide a solution to the problem of the infinite regress of justification. The solution turns on the falsity of two claims: that a belief is justified only if some belief is a reason for it, and that the reason relation is transitive.

This paper provides a thorough analysis of two often informally stated claims. First, successful argumentation in the sense of persuasive argumentation requires agreement between the interlocutors about the rationality of arguments. Second, a general agreement about rationality of arguments cannot itself be established via argumentation, since such an attempt leads to an infinite meta‐regress. Hence, agreement about the rationality of arguments is a precondition for successful argumentation. As the paper argues, these plausible claims hold under the assumption that interlocutors are (...) subjectively rational and follow their own standards of rationality when engaging in argumentation. (shrink)

How should you decide what to do when you're uncertain about basic normative principles (e.g., Kantianism vs. utilitarianism)? A natural suggestion is to follow some "second-order" norm: e.g., "comply with the first-order norm you regard as most probable" or "maximize expected choiceworthiness". But what if you're uncertain about second-order norms too -- must you then invoke some third-order norm? If so, it seems that any norm-guided response to normative uncertainty is doomed to a vicious regress. In this paper, I aim (...) to rescue second-order norms from this threat of regress. I first elaborate and defend the suggestion some philosophers have entertained that the regress problem forces us to accept normative externalism, the view that at least one norm is incumbent on agents regardless of their beliefs or evidence concerning that norm. But, I then argue, we need not accept externalism about first-order (e.g., moral) norms, thus closing off any question of what an agent should do in light of her normative beliefs. Rather, it is more plausible to ascribe external force to a single, second-order rational norm: the enkratic principle, correctly formulated. This modest form of externalism, I argue, is both intrinsically well-motivated and sufficient to head off the threat of regress. (shrink)

Simple regression (regression analysis with a single explanatory variable), and multiple regression (regression models with multiple explanatory variables), typically correspond to very different biological questions. The former use regression lines to describe univariate associations. The latter describe the partial, or direct, effects of multiple variables, conditioned on one another. We suspect that the superficial similarity of simple and multiple regression leads to confusion in their interpretation. A clear understanding of these methods is essential, as (...) they underlie a large range of procedures in common use in biology. Beyond simple and multiple regression in their most basic forms, understanding the key principles of these procedures is critical to understanding, and properly applying, many methods, such as mixed models, generalised models, and causal inference using graphs (including path analysis and its extensions). A simple, but careful, look at the distinction between these two analyses is valuable in its own right, and can also be used to clarify widely-held misconceptions about collinearity (correlations among explanatory variables). There is no general sense in which collinearity is a problem. We suspect that the perception of collinearity as a hindrance to analysis stems from misconceptions about interpretation of multiple regression models, and so we pursue discussions about these misconceptions in this light. In particular, collinearity causes multiple regression coefficients to be less precisely estimated than corresponding simple regression coefficients. This should not be interpreted as a problem, as it is perfectly natural that direct effects should be harder to characterise than univariate associations. Purported solutions to the perceived problems of collinearity are detrimental to most biological analyses. (shrink)

This paper presents a uniform general account of regress problems in the form of a pentalemma—i.e., a set of five mutually inconsistent claims. Specific regress problems can be analyzed as instances of such a general schema, and this Regress Pentalemma Schema can be employed to generate deductively valid arguments from the truth of a subset of four claims to the falsity of the fifth. Thus, a uniform account of the nature of regress problems allows for an improved understanding of specific (...) regress objections or arguments, and, correspondingly, of the general logical geography of the debate about infinite regresses. This uniform approach is illustrated by a treatment of the classical epistemological problem of justification, but it encompasses a whole variety of cases including explanation and ontological grounding. Furthermore, this general account is compared and contrasted with the existing literature discussing argument schemata for regress objections, particularly with the work of Jan Willem Wieland. It is shown how such other schemata can be incorporated and superseded by the general Regress Pentalemma Schema. (shrink)

Is there a successful regress argument against intellectualism? In this article I defend the negative answer. I begin by defending Stanley and Williamson's (2001) critique of the contemplation regress against Noë (2005). I then identify a new argument – the employment regress – that is designed to succeed where the contemplation regress fails, and which I take to be the most basic and plausible form of a regress argument against intellectualism. However, I argue that the employment regress still fails. Drawing (...) on the previous discussion, I criticise further regress arguments given by Hetherington (2006) and Noë (2005). (shrink)

This paper critically evaluates the regress argument for infinitism. The dialectic is essentially this. Peter Klein argues that only an infinitist can, without being dogmatic, enhance the credibility of a questioned non-evident proposition. In response, I demonstrate that a foundationalist can do this equally well. Furthermore, I explain how foundationalism can provide for infinite chains of justification. I conclude that the regress argument for infinitism should not convince us.

This chapter has two goals: to motivate the foundationalist solution to the regress problem and to defend it against arguments from Sellars, BonJour and Klein. Both the motivation and the defence of foundationalism raise larger questions about the relationship between foundationalism and access internalism. I argue that foundationalism is not in conflict with access internalism, despite influential arguments to the contrary, and that access internalism in fact supplies a theoretical motivation for foundationalism. I conclude that foundationalism and access internalism form (...) a coherent and well-motivated package. (shrink)

This paper explains what an infinite regress argument is. Part 1 contains some examples of infinite regress arguments. Part 2 presents a schema for all such arguments an defines an infinite regress argument as one that approximates to the schema. Part 3 tests the schema on the examples. Part 4 contrasts my account of infinite regress arguments with that given by Passmore and shows that Passmore's theory succumbs to objections. Part 5 distinguishes an infinite regress argument from an infinite regress (...) and defines an infinite regress. Part 6 explains the dyslogistic force of 'infinite regress'. (shrink)

We model infinite regress structures -not arguments- by means of ungrounded recursively defined functions in order to show that no such structure can perform the task of providing determination to the items composing it, that is, that no determination process containing an infinite regress structure is successful.