Narrow Results

This study traces short- to long-term adverse effects of the colossal flood 2010 on educational outcomes of children and adolescents (age 5–16 years) in the flooded districts of Pakistan. Taking advantage of the flood — a type of quasi-natural experimental research design we utilized a difference-in-differences (DID) approach with inverse probability of treatment weights (IPTWs) to estimate the impact of the flood on educational outcomes by using a household surveys’ dataset (six waves). We compare educational outcomes out-of-school or dropout from school of — children and adolescents in the flooded households with the educational outcomes of individuals of same age groups in the non-flooded households before, during and after the flood. Our findings reveal that, on an average, 39 out of 1000 children and adolescents in the flooded districts, compared with their counterparts in the non-flooded districts, were not admitted in any educational institutions and 16 of them dropped out from schools during the flood. The effect of flood on education of children and adolescents, then, disappeared after 2–4 years after the flood. The education outcomes of children and adolescents in flooded households in rural areas compared with their peers in non-flooded districts were severely affected by the flood. Mirroring the impact of flood on education sector to the current heavy flood 2022 in Pakistan or pandemic COVID-19 is similarly compelling nations around the world for closure of their schools and educational institutions. The findings of this study may have some policy implications in terms of identifying the most vulnerable children and adolescents to mitigate the adverse impact of the natural disasters such as flood or pandemic on education outcomes and particularly significant to pinpoint shocks of disasters that have large and long-run impacts on human capital accumulation.

Using data from the 2007 Timor-Leste Living Standards Survey, this paper examines the determinants of household energy choices in Timor-Leste. The majority of households are dependent on dirty fuels such as fuelwood and kerosene for energy. Only a small fraction of households use clean energy such as electricity. Econometric results show that wealthy households, urban households, and those headed by individuals with higher levels of education are less likely to use and depend on kerosene and more likely to use and depend on electricity. While female-headed households are generally more likely to use and depend on fuelwood, richer female-headed households are more likely to use and depend on electricity. Our findings highlight the importance of ensuring an adequate supply of clean energy for all at affordable prices and of investing in education to raise awareness about the adverse impacts of using dirty fuels.

Households in developing countries predominantly rely on solid fuel for cooking, which is injurious to both the environment and human health. The provision of clean energy for cooking, therefore, is essential for safeguarding the environment and human health, primarily of women and children in developing countries. Using the 2014–2015 Pakistan Social and Living Standards Measurement Survey and robust econometric methods, this study analyzes different types of energy used for cooking among urban households in Pakistan. The study shows that although urban households in Pakistan mostly use gas for cooking, the use of solid fuels, particularly among poor and relatively less educated households, is pervasive. The econometric findings confirm that households with a higher level of education and wealthy families mainly use clean energy, such as gas, and are less likely to use dirty solid fuels, such as cake dung and crop residue for cooking. Considering the expansion of middle-class households and anticipating their demand for clean fuel for cooking, this study suggests ensuring an adequate supply of clean sources of energy to meet future demand as well as augmenting the affordability and awareness among households who are still dependent on solid fuels.

Learning analytics, as a rapidly evolving field, offers an encouraging approach with the aim of understanding, optimizing and enhancing learning process. Learners have the capabilities to interact with the learning analytics system through adequate user interface. Such systems enables various features such as learning recommendations, visualizations, reminders, rating and self-assessments possibilities. This paper proposes a framework for learning analytics aimed to improve personalized learning environments, encouraging the learner’s skills to monitor, adapt, and improve their own learning. It is an attempt to articulate the characterizing properties that reveals the association between learning analytics and personalized learning environment. In order to verify data analysis approaches and to determine the validity and accuracy of a learning analytics, and its corresponding to learning profiles, a case study was performed. The findings indicate that educational data for learning analytics are context specific and variables carry different meanings and can have different implications on learning success prediction.

This study focused on the trilemma association of education, income and poverty alleviation: managerial implications for inclusive economic growth in developing countries in Asia to establish the proportion of the poor in the population and further identify its determinants. This research utilized secondary data from 1990 to 2016 by using econometric estimation. The results show that education decreases poverty when evaluated through the poverty gap and poverty headcount ratio and employment and increasing rate of economic development in the form of GDP to reducing poverty. GDP the Gini coefficient show the same signs while the magnitudes of the coefficients. Consequently, improvement in an independent variable will decrease poverty while the results have various levels of contributions through static and dynamic panel data methods, that education can reduce poverty. Results indicate that the level of poverty stood at 62.2%. The level of education, poverty headcount ratio, poverty gap and secondary school enrolment were significant in determining a household’s poverty status. However, land ownership and household head’s occupation were not statistically significant in explaining the probability of a household’s poverty status. From the results, this study recommends that all stakeholders work towards reducing poverty in the study to enhance education and family planning.

This paper analyzes the equity of opportunity in basic education and infrastructure services in seven developing countries, Bangladesh, Bhutan, Indonesia, Pakistan, the Philippines, Sri Lanka, and Viet Nam. The analysis applies a method developed by the World Bank called the Human Opportunity Index, which measures the total contribution of individual socioeconomic and demographic circumstances to inequality of opportunity in accessing basic services. The new and major contribution of the paper, however, is the development of a methodology that quantifies the relative contribution of each circumstance variable to the inequality of opportunity. This contribution is crucial in identifying which underlying inequalities matter most—which can have important policy implications, for instance, in terms of developing better-targeted interventions. Results of the empirical analysis indicate that more needs to be done to improve the distribution of economic benefits. Opportunities to access basic education and infrastructure services in the seven countries vary widely in terms of availability and distribution. The study also finds that inequality of opportunity is driven mainly by per capita household expenditure. This suggests that household poverty plays a crucial role in determining equitable access to basic services.

Recent decades have witnessed an unprecedented expansion of democracy. During the third wave of democratization, as described by Samuel Huntington, democracy spread well beyond its historical boundaries and it is now adopted in all major regions of the world. Yet, not all democracies are equally effectual in delivering good governance and progrowth policies. Why do democratic institutions induce good governance and prosperity only in some economies? This paper presents an overview of the dimensions along which successful and unsuccessful democracies differ. It argues that four socioeconomic variables are of critical importance to create and maintain a well-functioning democracy: (i) social capital, (ii) information, (iii) education, and (iv) equality. History also plays an important role as do the contingencies characterizing the collapse of authoritarian regimes and the emergence of democratic institutions.

Using 2007–2010 data from Thailand's National Labor Force Survey, this paper examines the rates of return to schooling. The Mincer-type rate of return to investment in schooling was estimated. The rates of return to schooling for work experience are significantly positive, but at a decreasing rate. Region of residence and variation in gross provincial product per capita are significant factors in determining the private rate of return. The rates of return to schooling by type of industry reveal higher earnings in mining, utilities, construction, manufacturing, and services than in agriculture. The private and social returns on vocational secondary education attainment are greater than on general secondary education. Finally, the private returns on university attainment for women exceed men by about 1.5 percentage points.

We pursue a cross-country comparison of relative financial readiness of older households in Japan and the Republic of Korea relative to the United States. Our comparative analysis, using macro-level and harmonized longitudinal household financial data, covers the principal financial channels of old-age support: public and private pension plans, family support, and self-management of private financial portfolios. We find that while all three countries have similar public pension systems, older Americans benefit from more developed and better-funded public and private pension systems, as well as individual management of risky financial portfolios. We find that educational and health attainments of household heads and household wealth lead to a greater tendency to hold and manage risky assets. Our decomposition analysis also shows that the gap in stock ownership in Asian countries relative to the United States can be attributed to lower levels of development in financial and pension markets. However, these gaps have been shrinking more recently.

This study is conducted to examine the effect on income inequality of government spending on education across 63 provinces in Vietnam. The generalized method of moments (GMM) regression technique is used to address potential endogeneity in the model caused by income inequality and inequality in government spending on education. Income inequality is proxied by both the Gini coefficient and the Theil index. Inequality in government spending on education in Vietnam is estimated using a novel entropic approach, which decomposes the inequality into two components: “within-province” inequality and “between-province” inequality. Data for the period from 2010 to 2016 are used. Our empirical findings are summarized as follows. First, “within-province” inequality accounts for a substantial portion of inequality in government spending on education. This means that although the Vietnamese national government has done well in terms of allocating spending on education across 63 provinces, inequality in education spending appears across districts within provinces. Second, both total inequality of government spending on education and its two components are positively associated with income inequality across provinces. As such, reducing differences in government spending on education across provinces and across districts within provinces is an effective mechanism for reducing income inequality across provinces and across districts within provinces in Vietnam.

This paper aims to reassess multidimensional poverty measurement including the ease of doing business as an additional indicator with the existing measurements for 81 countries by human development, and identify how multidimensional poverty has changed during a very short period from 2014 to 2017. Further, using the tobit regression model, this study reveals the determinants of multidimensional poverty and its major indicators for both the periods. Results reveal that low human development countries are likely to be exposed to the highest multidimensional poverty as compared to moderate, high and very high human development countries. Surprisingly, we found that reduction of multidimensional poverty between 2014 and 2017 was the highest in moderate human development countries (8.18%), followed by high (5.27%), very high (3.94%) and low (2.67%) human development countries. Further, the findings from the regression results suggest that variables such as Global Climatic Risk index, Total Natural Resource Rents, Age Dependency Ratio and Urban Population Growth have a significant and positive impact on inducing multidimensional poverty irrespective of any group of countries. Contrastingly, Labour Force Participation Rate, higher score of Food Production Index, Personal Remittances Received and Volume of Trade significantly and negatively influence multidimensional poverty across the group of countries. As per the regression results, agricultural and external sectors (Food Production Index, Agricultural Land, Personal Remittances Received and Trade Volume) play a major role in reducing multidimensional poverty. This study will be helpful for the policy purpose to achieve the Sustainable Development Goals (SDGs) for the specific group of countries (SDGs 1–4, 6 and 7). Policy measures must focus largely on investment in the human capital along with prioritising climate risk reduction, proper planned urbanisation and strengthening legal rights for the vulnerable section of the people.

In rural areas of developing countries, shocks and financial constraints on households are generally recognized as obstacles to children’s schooling opportunities. This paper investigates the effects of income shocks and borrowing constraints on household demand for education in rural Thailand, using the Townsend Thai panel data spanning from 2013 to 2017. Information on annual rainfall at the provincial level is used to estimate a transitory income component for Thai rural households. Estimation results indicate that income risks and borrowing constraints have a substantial negative impact on child schooling outcomes, including educational attainment and the number of years delayed in school. It also finds that transitory income results in increased household education expenditures conditional on children’s attendance at school. These findings suggest that in addition to households’ socioeconomic status, children’s human capital is at risk mainly due to income uncertainty and the absence of well-developed financial and insurance markets.

Inequality in access to education is known to be a key driver of income inequality in developing countries. Viet Nam, a transitional economy, exhibits significant segmentation in the market for skilled labor based on more remunerative employment in government and state firms. We ask whether this segmentation is also reflected in human capital investments at the household level. We find that households whose heads hold state jobs keep their children in school longer, spend more on education, and are more likely to enroll their children in tertiary institutions relative to households whose heads hold nonstate jobs. The estimates are robust to a wide range of household and individual controls. Over time, disparities in educational investments based on differential access to jobs that reward skills and/or credentials help widen existing income and earnings gaps between well-connected “princelings” and the rest of the labor market. Capital market policies that create segmentation in the market for skills also crowd out investment in private sector firms, further reducing incentives for human capital deepening.

Artificial intelligence (AI) has witnessed significant advancements, reshaping various industries, including healthcare. The introduction of ChatGPT by OpenAI in November 2022 marked a pivotal moment, showcasing the potential of generative AI in revolutionising patient care, diagnosis and treatment. Generative AI, unlike traditional AI systems, possesses the ability to generate new content by understanding patterns within datasets. This article explores the evolution of AI in healthcare, tracing its roots to the term coined by John McCarthy in 1955 and the contributions of pioneers like John Von Neumann and Alan Turing. Currently, generative AI, particularly Large Language Models, holds promise across three broad categories in healthcare: patient care, education and research. In patient care, it offers solutions in clinical document management, diagnostic support and operative planning. Notable advancements include Microsoft’s collaboration with Epic for integrating AI into electronic medical records (EMRs), enhancing clinical data management and patient care. Furthermore, generative AI aids in surgical decision-making, as demonstrated in plastic, orthopaedic and hepatobiliary surgeries. However, challenges such as bias, hallucination and integration with EMR systems necessitate caution and ongoing evaluation. The article also presents insights from the implementation of NUHS Russell-GPT, a generative AI chatbot, in a hand surgery department, showcasing its utility in administrative tasks but highlighting challenges in surgical planning and EMR integration. The survey showed unanimous support for incorporating AI into clinical settings, with all respondents being open to its use. In conclusion, generative AI is poised to enhance patient care and ease physician workloads, starting with automating administrative tasks and evolving to inform diagnoses, tailored treatment plans, as well as aid in surgical planning. As healthcare systems navigate the complexities of integrating AI, the potential benefits for both physicians and patients remain significant, offering a glimpse into a future where AI transforms healthcare delivery.

Level of Evidence: Level V (Diagnostic)

Large Language Models (LLMs) are a type of artificial intelligence that has been revolutionizing various fields, including biomedicine. They have the capability to process and analyze large amounts of data, understand natural language, and generate new content, making them highly desirable in many biomedical applications and beyond. In this workshop, we aim to introduce the attendees to an in-depth understanding of the rise of LLMs in biomedicine, and how they are being used to drive innovation and improve outcomes in the field, along with associated challenges and pitfalls.

Entrepreneurs are a product of their social environment. The manner by which they perceive opportunities; access or process information; and make decisions is, influenced by both social interaction, and their social background. Using insights from Socially Situated Cognition (SSC) theory, that posits one’s social environment can have a normative or informative effect on decision-making process we consider proximal social factors influencing the decision-making processes of student entrepreneurs. We propose that entrepreneurial education, networking, and incubation spaces provide direct information to students to aid entrepreneurial decision-making, and indirect informational cues that are situational, synergistic and omnipresent. Noting the multi-faceted and dynamic nature of the entrepreneurial journey of the student, we explore the potential effect of each of these factors on the student decision-making process. We discuss the implications of this inquiry from a researcher and educator perspective, and note the current challenges faced by student entrepreneurs in a socially distanced educational and entrepreneurial context. It is envisaged that this paper will serve as the basis for further thought and empiricism.

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

We present evidence against the well-established education–health gradient by relating education to measured hypertension status in 5,873 men and 6,152 women aged 40$+$ in Indonesia. Once a basic set of covariates was controlled for, the two variables were not statistically significantly related. We argue that this lack was due to neglect of chronic diseases. It appears that the assumption of full information in theories on the education–health gradient is too strong to be applied to the developing world. Therefore, more information needs to be provided to the public regarding the seriousness of chronic diseases and preventive and curative methods.

Large language models (LLMs) and biomedical annotations have a symbiotic relationship. LLMs rely on high-quality annotations for training and/or fine-tuning for specific biomedical tasks. These annotations are traditionally generated through expensive and time-consuming human curation. Meanwhile LLMs can also be used to accelerate the process of curation, thus simplifying the process, and potentially creating a virtuous feedback loop. However, their use also introduces new limitations and risks, which are as important to consider as the opportunities they offer. In this workshop, we will review the process that has led to the current rise of LLMs in several fields, and in particular in biomedicine, and discuss specifically the opportunities and pitfalls when they are applied to biomedical annotation and curation.

This study investigates Beijing’s use of educational initiatives as a tool for soft power in Pakistan. It explores how these initiatives influence various stakeholders within Pakistan. The research employs a descriptive analysis approach. It examines the mechanisms China utilizes through its educational system to build relationships with Pakistan, promote Chinese culture, and shape Pakistani perceptions of China. Beijing leverages its educational system through four key mechanisms to exert soft power in Pakistan. Confucius Institutes offer Chinese language courses and cultural events, fostering direct engagement with Chinese culture. University partnerships facilitate knowledge exchange and joint research collaboration between Chinese and Pakistani universities. Scholarship programs attract Pakistani students to study in China, providing them with language skills, cultural understanding, and potential career opportunities. Finally, the internationalization of Chinese universities, offering English-taught programs, makes them more accessible to Pakistani students. The impact of these initiatives varies across stakeholder groups. Students and academics directly benefit by gaining language skills, cultural understanding, and potential career opportunities. However, concerns exist about ideological influence, academic freedom, and brain drain. Broader Pakistani society experiences indirect exposure through media, interactions with graduates, and changing perceptions of China.