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This study aimed to compare mortality rate, length of stay (LOS) and hospitalization costs at different priority levels for a patient admitted to an intensive care unit (ICU) at a public tertiary hospital in Qazvin, Iran. This study also aimed to predict influencing factors on patients’ mortality, ICU LOS and hospitalization costs in different admission groups.

The authors conducted a retrospective cohort study among patients who mainly suffered from internal diseases admitted to an ICU of a public hospital. This study was conducted among 127 patients admitted to ICU from July to September 2019. The authors categorized patients into four groups based on two crucial hemodynamic and respiratory status criteria. The authors used a logistic regression model to predict the likelihood of mortality in ICU admitted patients during hospitalizations for the four prioritization groups. Furthermore, the authors conducted a multivariate analysis using the “enter” method to identify risk factors for LOS.

Results showed a statistically significant relationship between the priority of being admitted to ICU and hospitalization costs. The authors’ findings revealed that age, LOS and levels of consciousness had a predictability role in determining in-hospital mortality. Besides, age, gender, consciousness level of patients and type of the disease were mentioned as affecting factors of LOS.

This study’s findings emphasize the necessity of categorizing patients according to specific criteria to efficiently use available resources to help health-care authorities reduce the costs and allocate the budget to different health sectors.

The current study investigates the impact of directors' attributes on the extent of compliance with International Financial Reporting Standards (IFRS) fair value disclosure requirements. The attributes investigated include directors' human capital (accounting qualification) and social capital (political association), directors' share ownership and the power distance between the chief executive officer (CEO) and the rest of the board members.

The study uses disclosure analysis to measure the extent of compliance with the fair value disclosure requirements of IFRS. Ordinary least squares (OLS) regression is used to test the relationship between the disclosure score and directors' attributes. Data were collected from the annual reports and websites of the sample companies.

Contrary to conventional belief, this study's findings suggest that directors' social capital and the power distance between the CEO and the rest of the board act as more powerful factors than directors' human capital in explaining corporate mandatory disclosure. Specifically, the results indicate that powerful actors form a dominant coalition and co-opt influential constituents from the institutional domain to neutralize the effect of legal coercion and the accounting expertise of board members and Big Four audit firms on the extent of compliance with institutional (fair value) rules.

This study utilizes Oliver's (1991) framework of strategic response to institutional processes in the Bangladeshi context. Although the study provides new insights into corporate disclosure practices, findings are not generalizable due to different institutional settings in different countries. Therefore, future studies could replicate the approach in different institutional settings.

The findings of this study will be of interest to the International Accounting Standards Board (IASB) as it focuses on a developing country that has adopted IFRS 13 and other fair value-related standards relatively recently.

The disclosure analysis contained in this study represents the first comprehensive analysis of the extent of compliance with the fair value disclosure requirements of IFRS. Furthermore, this study considers the impact of directors' social capital and finds that it is a more powerful determinant of the extent of compliance with IFRS as compared to human capital.

This paper aims to classify tourist accommodation using data from Booking.com and TripAdvisor and analyse the extent to which the different segments identified differ in terms of being adults-only.

In total, 1,535 properties located in nine Spanish sun and beach destinations were examined using a latent class cluster analysis (LCCA). The bias-adjusted three-step approach was used to investigate the differences between belonging to adults-only accommodation or not among the identified clusters.

Results show that adults-only accommodation tends to belong to the cluster with higher online ratings. In small Spanish islands, adults-only hotels account for a large share (more than 25%) of hotels.

It was not possible to analyse whether the higher rating was due to the accommodation being better or due to the tourists being more satisfied with their stay.

In urban destinations, the model is not widely used. However, in coastal destinations, it is becoming more than a novelty or a new trend.

In small Spanish islands, people traveling with children are becoming a minority. Families may feel discriminated against and express dissatisfaction with this situation in the future.

This study covers the gap in the academic literature on this growing hotel segment.

The purpose of this research was first to determine the competencies mandatory of risk managers, and second, to consider the implications of such competencies in determining modules appropriate for inclusion in any prospective undergraduate qualification with specialisation in risk management.

A qualitative research approach was followed, involving academics teaching risk management in a focus group and making use of interactive qualitative analysis (IQA).

The competencies identified were business management skills, financial knowledge, an understanding of the risk management process, governance and compliance, people management and technical skills. These will be explained in greater detail in the paper.

The implications for teaching are that an undergraduate curriculum in risk management will have to combine majors such as business management, financial management, risk management, industrial psychology and communication. These majors need to be complemented by modules in governance and compliance management, as well as information and communication technology.

The implication for practice is that risk management professionals and members of the Institute of Risk Management of South Africa need to avail themselves to serve on an advisory board of academic departments offering risk management qualifications. Risk management is a developing science and requires inputs about research and the curriculation of qualifications.

The implication for public policy is that the South African Qualifications Authority and the Council for Higher Education should reconsider their requirements for designators (specialised qualifications). The implications for research are that IQA provides clarity on the knowledge and skills required to develop a competency-based qualification in risk management. Further research should benchmark qualifications and propose a curriculum for a bachelor’s degree in risk management.

The use of IQA is a novel way of ensuring rigour and objectivity in arriving at a description of the required knowledge, skills, values and attributes of risk managers. This paper will assist in the compilation of a new curriculum for an undergraduate qualification in risk management; thus, ensuring such qualification will provide a competency-based qualification that will meet the needs of the profession.

This paper aims to provide a historical overview of AA, its purpose and benefits, the legal rationale for the SCOTUS ruling and what it means for colleges and the workplace regarding equitable opportunities for minority groups (which include women, Blacks, Hispanics, Asians and other low-income populations), as they aim for the “American dream”.

SCOTUS decision and rationale, along with literature.

The race-based affirmative action (AA) precedent was recently overturned by the Supreme Court of the United States (SCOTUS) in the case of Students for Fair Admission (SFFA), Inc. vs President and Fellows of Harvard College/University of North Carolina. SCOTUS ruled that race cannot be a specific basis for college admission. In other words, public and private colleges and universities will no longer be able to consider “race” as a factor in deciding which qualified applicants should be admitted to enhance the diversity of their student body.

This is an original analysis.

The uncertainty of getting admission into universities/institutions is one of the global problems in an academic environment. The students are having good marks with highest credential, but they are not sure about getting their admission into universities/institutions. In this research study, the researcher builds a predictive model using Naïve Bayes classifiers – machine learning algorithm to extract and analyze hidden pattern in students’ academic records and their credentials. The main purpose of this research study is to reduce the uncertainty for getting admission into universities/institutions based on their previous credentials and some other essential parameters.

This research study presents a joint venture of Naïve Bayes Classification and Kernel Density Estimations (KDE) to predict the student’s admission into universities or any higher institutions. The researcher collected data from the Kaggle data sets based on grade point average (GPA), graduate record examinations (GRE) and RANK of universities which are essential to take admission in higher education.

The classification model is built on the training data set of students’ examination score such as GPA, GRE, RANK and some other essential features that offered the admission with a predictive accuracy rate 72% and has been experimentally verified. To improve the quality of accuracy, the researcher used the Shapiro–Walk Normality Test and Gaussian distribution on large data sets.

The limitation of this research study is that the developed predictive model is not applicable for getting admission into all courses. The researcher used the limited data attributes such as GRE, GPA and RANK which does not define the admission into all possible courses. It is stated that it is applicable only for student’s admission into universities/institutions, and the researcher used only three attributes of admission parameters, namely, GRE, GPA and RANK.

The researcher used the Naïve Bayes classifiers and KDE machine learning algorithms to develop a predictive model which is more reliable and efficient to classify the admission category (Admitted/Not Admitted) into universities/institutions. During the research study, the researcher found that accuracy performance of the predictive Model 1 and that of predictive Model 2 are very close to each other, with predictive Model 1 having truly predictive and falsely predictive rate of 70.46% and 29.53%, respectively.

Yes, it is having a significant contribution for society; students and parents can get prior information about the possibilities of admission in higher academic institutions and universities.

The classification model can reduce the admission uncertainty and enhance the university’s decision-making capabilities. The significance of this research study is to reduce human intervention for making decisions with respect to the student’s admission into universities or any higher academic institutions, and it demonstrates many universities and higher-level institutions could use this predictive model to improve their admission process without human intervention.

Although there has been considerable growth in the higher education systems of Turkey and China in about the last two decades, there is still a room for development in enabling equity in all regions, increasing opportunities and resources regardless of socio-economic status or gender differences. Students coming from disadvantaged backgrounds do not have enough tools to change their fate for the better due to the accumulated barriers they face. Given this background, the chapter discusses how the barriers to equitable HE admissions relate to each other and which one has a greater negative impact over the Accumulated Conversion Barriers Modal we propose defined by personal, discriminatory, institutional, and geographical barriers. The perspectives of Turkish and Chinese HE stakeholders were examined through 21 in-depth interviews that were subjected to content analysis and interpreted in a comparative style using the lens of Capabilities Approach of Sen. We also offer policy suggestions to increase students’ conversion capacities for better outcomes to serve both the national and the international educational contexts owing to the adaptable nature of our modal to other countries experiencing similar issues in their higher education systems.

The purpose of this study is to implement lean six sigma (LSS) methodology to improve the admission process in a higher education institute (HEI).

In this study, case study research methodology is adopted and implemented through an LSS define-measure-analyze-improve-control (DMAIC) framework.

The preliminary investigation showed that the completion of the whole admission process of a new student takes an average of 88 min, which is equivalent to a sigma level of about 0.71 based on the targeted admission cycle time of 60 min. The implementation of the proposed LSS approach increased the sigma level from 0.71 to 2.57, which indicates a reduction in the mean admission cycle time by around 55%. This substantial improvement is expected not only to provide an efficient admission process but also to enhance the satisfaction of students and employees and increase the reputation of the HEI to a significant level.

In this study, the sample size used in the analysis is considered small. In addition, the effectiveness of the proposed approach is investigated using a discrete event simulation with a single-case study, which may limit generalization of the results. However, this study can provide useful guidance for further research for the generalization of the results to wider scopes in terms of different sectors of HEIs and geographical locations.

This study uses several statistical process control tools and techniques through a LSS DMAIC framework to identify and element the root causes of the long admission cycle time at a HEI. The approach followed, and the lessons learned, as documented in the study, can be of a great benefit in improving different sectors of HEIs.

This study is one of the few attempts to implement LSS in HEIs to improve the administrative process so that better-quality services can be provided to customers, such as students and guardians. The project is implemented by a group of undergraduate students as a part of their senior design project, which paves the way for involving students in future LSS projects in HEIs. This study is expected to help to improve understanding of how LSS methodology can be implemented in solving quality-related problems in HEIs and to offer valuable insights for both academics and practitioners.

The purpose of this study is to examine whether implicit bias exists within the graduate admissions process at a large public research university in the Southeast United States. Additionally, this research sought to identify the type of strategies graduate faculty in the USA use to assess their implicit bias and the support they may need to better recognize and gauge implicit bias during the graduate application review process.

This study used the use of a qualitative, phenomenological research design by conducting individual interviews with graduate faculty members that serve on admissions committees.

The findings revealed six themes in relation to the purpose of the study – bias recognition, faculty perceptions of their own bias, faculty perceptions on the bias of others, strategies for the application review process, admission committee safeguards and the need for implicit bias training.

The study outcomes are discussed in relation to the prior research and literature on this phenomenon. Additionally, the study presents research and practical implications, including actionable strategies for how its results can be practically applied.

Education can be classified into formal and informal sectors—the first category as a regular schooling system and the latter category as private tutoring. After completing secondary education, students in many countries receive education from private tutoring to get admission into the university. This study examines the effect of private tutoring on university admission and subsequent students' academic achievement at the university level.

Using survey data from Bangladesh as a case study, this study employs a two-stage least squares (2SLS) methodology.

Considering that coaching centers offer services such as private tutoring, this study finds that an informal education for admission greatly helps academic achievement. Students who benefit from informal schooling are more likely to achieve higher grades in subsequent programs.

This study strongly suggests that formal education at the secondary school level is unable to meet the academic expectations that are demanded at the tertiary level. This forces the development of private tutoring, which supports the students from more financially well-off families to perform well at the cost of educational disparity.