Methodological Gaps Research Articles

Estimating the higher heating value and chemical exergy of solid, liquid, and natural gas fossil fuels

The escalating global population has spurred a significant surge in energy demand. While developed nations pivot towards renewable energy sources, many developing countries rely heavily on fossil fuels. Understanding these fuels' higher heating value and chemical exergy is crucial for energy analysis, second law analysis, optimal energy generation, and resource conservation.This study introduces an adaptable analytical method for calculating the higher heating value and chemical exergy of fossil fuels (coal, oil, natural gas). It demonstrates minimal error, with approximately a 3.5 % deviation for higher heating value and 8 % for chemical exergy compared to empirical relations.Novel relationships, developed through linear regression and computer programming, address research gaps and exhibit a maximum 2.6 % difference for higher heating value and 5.4 % for chemical exergy compared to existing empirical references. These contributions will greatly benefit energy professionals and engineers.This research addresses critical gaps in current methodologies for estimating higher heating value and exergy by offering a compact form for calculating various fossil fuels within a wide range of Carbon-to-Hydrogen ratios (3–105). Additionally, it provides specific equations applicable to both mole and mass fractions for natural gases, primarily composed of methane, with mole fractions ranging from 55 % to 99 %.

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010–2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

Making stateless and stateful network performance measurements unbiased

The Benchmarking Working Group (BMWG) of the Internet Engineering Task Force (IETF) has defined a series of Requests for Comments (RFC) to standardize the benchmarking of network interconnect devices (e.g., bridges, routers, different IPv6 transition solutions). The paper points out that there are cases where the performance results are significantly different when a single IP address pair or multiple IP addresses are used. The cause of this phenomenon is rooted in the recent hardware and software advancements: Receive Side Scaling (RSS) makes it possible to distribute packet processing workload over multiple CPU cores. However, this may be implemented in two ways: the first way only includes the IP addresses into the hash function used to distribute the workload among the CPU cores, whereas the second one also includes the port numbers. RFC 4814 proposed an excellent solution for the second case by recommending the usage of pseudorandom port numbers during benchmarking; however, the first case was not handled properly, because no explicit recommendation was given regarding the usage of multiple IP addresses. This paper attempts to bridge this methodological gap; a practical solution is proposed for using pseudorandom IP addresses in various scenarios including the benchmarking of IPv4 and IPv6 routers and Network Address Translation from IPv6 Clients to IPv4 Servers (stateful NAT64) gateways. Its feasibility is shown by disclosing the details of its implementation in siitperf. Then the proposed solution is validated by both stateless and stateful tests. It is shown that the measurement results of the tests following the proposed solution can better characterize the true performance of the network interconnect devices that follow the first type of RSS implementation than the results of the tests using a single IP address pair.

Investigating the Determinants that Influence Consent Behavior for Linking Survey Data with Administrative Records

This research investigates the determinants of consent behavior in linking survey and administrative data, filling a notable gap in existing studies. It conducts a thorough systematic review of a wide variety of surveys from multiple countries, examining survey and consent design characteristics to assess their influence on consent rates. Through statistical analysis, the study evaluates numerous factors such as survey response rate, topic, country, year, type, mode, age of target population, sponsor, and various aspects related to the consent request process. The findings suggest that higher consent rates are more likely in computer assisted telephone interview (CATI) compared to self-administered surveys, in surveys sponsored by governments, in panel surveys as opposed to cross-sectional ones, and where explicit consent is sought, especially when the consent request is made at the start of the survey. This research makes a significant contribution to academic literature by empirically demonstrating the effect of various survey and consent design features on consent behavior, providing essential insights for crafting surveys to maximize consent rates for data linkage. It closes an important research gap in survey methodology and the behavior on data linkage consent, offering a unique, extensive analysis across a broad range of international surveys.

Remote sensing applications for prescribed burn research

Prescribed burning is a key management strategy within fire-adapted systems, and improved monitoring approaches are needed to evaluate its effectiveness in achieving social-ecological outcomes. Remote sensing provides opportunities to analyse the impacts of prescribed burning, yet a comprehensive understanding of the applications of remote sensing for prescribed burn research is lacking. We conduct a literature review of 120 peer-reviewed publications to synthesise the research aims, methodologies, limitations and future directions of remote sensing for the analysis of prescribed fire. Studies evaluating management outcomes found prescribed burning effective for wildfire risk reduction, yet few analysed co-benefits or trade-offs with other management goals. Most studies use passive, spaceborne, low spatial resolution sensors, characterised in the literature as consistent and accessible data sources but limited in detecting small, low-severity and short-duration fires characteristic of prescribed burns. In contrast, active remote sensing approaches including LiDAR are less frequently employed, but show promise for highly accurate, spatially explicit 3D vegetation and fuel load mapping. Remote sensing advances toward higher spatial resolution, more frequent revisit, denser spectral sampling and more data across the electromagnetic spectrum are critical to advancing prescribed fire research, addressing current methodological gaps, and improving fuels and fire management capacity.

Exploring undiscovered public knowledge in neuroscience.

In this essay, I argue that the combination of research synthesis and philosophical methods can fill an important methodological gap in neuroscience. While experimental research and formal modelling have seen their methods progressively increase in rigour and sophistication over the years, the task of analysing and synthesizing the vast literature reporting new results and models has lagged behind. The problem is aggravated because neuroscience has grown and expanded into a vast mosaic of related but partially independent subfields, each with their own literatures. This fragmentation not only makes it difficult to see the full picture emerging from neuroscience research but also limits progress in individual subfields. The current neuroscience literature has the perfect conditions to create what the information scientist Don Swanson called "undiscovered public knowledge"-knowledge that exists in the mutual implications of different published pieces of information but that is nonetheless undiscovered because those pieces have not been explicitly connected. Current methods for rigorous research synthesis, such as systematic reviews and meta-analyses, mostly focus on combining similar studies and are not suited for exploring undiscovered public knowledge. To that aim, they need to be adapted and supplemented. I argue that successful exploration of the hidden implications in the neuroscience literature will require the combination of these adapted research synthesis methods with philosophical methods for rigorous (and creative) analysis and synthesis.

Pairwise summation as a method for the additive combination of probabilities in qualitative risk assessments.

Qualitative frameworks are widely employed to tackle urgent animal or public health issues when data are scarce and/or urgent decisions need to be made. In qualitative models, the degree of belief regarding the probabilities of the events occurring along the risk pathway(s) and the outcomes is described in nonnumerical terms, typically using words such as Low, Medium, or High. The main methodological challenge, intrinsic in qualitative models, relates to performing mathematical operations and adherence to the rule of probabilities when probabilities are nonnumerical. Although methods to obtain the qualitative probability from the conditional realization of n events are well-established and consistent with the multiplication rule of probabilities, there is a lack of accepted methods for addressing situations where the probability of an event occurring can increase, and the rule of probability P(AUB)=P(A)+P(B)-P(A∩B) should apply. In this work, we propose a method based on the pairwise summation to fill this methodological gap. Our method was tested on two qualitative models and compared by means of scenario analysis to other approaches found in literature. The qualitative nature of the models prevented formal validation; however, when using the pairwise summation, results consistently appeared more coherent with probability rules. Even if the final qualitative estimate can only represent an approximation of the actual probability of the event occurring, qualitative models have proven to be effective in providing scientific-based evidence to support decision-making. The method proposed in this study contributes to reducing the subjectivity that characterizes qualitative models, improving transparency and reproducibility.

Causal fairness assessment of treatment allocation with electronic health records

Objective:Healthcare continues to grapple with the persistent issue of treatment disparities, sparking concerns regarding the equitable allocation of treatments in clinical practice. While various fairness metrics have emerged to assess fairness in decision-making processes, a growing focus has been on causality-based fairness concepts due to their capacity to mitigate confounding effects and reason about bias. However, the application of causal fairness notions in evaluating the fairness of clinical decision-making with electronic health record (EHR) data remains an understudied domain. This study aims to address the methodological gap in assessing causal fairness of treatment allocation with electronic health records data. In addition, we investigate the impact of social determinants of health on the assessment of causal fairness of treatment allocation. Methods:We propose a causal fairness algorithm to assess fairness in clinical decision-making. Our algorithm accounts for the heterogeneity of patient populations and identifies potential unfairness in treatment allocation by conditioning on patients who have the same likelihood to benefit from the treatment. We apply this framework to a patient cohort with coronary artery disease derived from an EHR database to evaluate the fairness of treatment decisions. Results:Our analysis reveals notable disparities in coronary artery bypass grafting (CABG) allocation among different patient groups. Women were found to be 4.4%–7.7% less likely to receive CABG than men in two out of four treatment response strata. Similarly, Black or African American patients were 5.4%–8.7% less likely to receive CABG than others in three out of four response strata. These results were similar when social determinants of health (insurance and area deprivation index) were dropped from the algorithm. These findings highlight the presence of disparities in treatment allocation among similar patients, suggesting potential unfairness in the clinical decision-making process. Conclusion:This study introduces a novel approach for assessing the fairness of treatment allocation in healthcare. By incorporating responses to treatment into fairness framework, our method explores the potential of quantifying fairness from a causal perspective using EHR data. Our research advances the methodological development of fairness assessment in healthcare and highlight the importance of causality in determining treatment fairness.

Implementing AI in the public sector

ABSTRACT Artificial Intelligence (AI) has advanced as one of the most prominent technological innovations to push the conversation about the digital transformation of the public sector forward. This special issue focuses on actual implementation approaches or challenges that public managers are facing while they fulfil new policy that asks for the implementation of AI in public administrations. In addition to assessing the contributions of papers in this issue, we also provide a research agenda on how future research can fill some of the methodological, theoretical, and application gaps in the public management literature.

Data-Driven Predictive Analysis and Sustainable Management of Concrete Waste in Pakistan

The construction sector of Pakistan is on a cross-growth trajectory, developing under the twin pressures of emerging infrastructure-based demands and sustainable practices that need to be inculcated urgently. This article focuses on the critical evaluation of sustainable waste management practices within the fast-developing construction industry of Pakistan, and clearly delineates a research gap in the current methodologies and use of data combined with the absence of a strategy for effective management of concrete waste. This research aims to utilize an algorithm based on machine learning that will provide accurate prediction in the generation of construction waste by harnessing the potential of real-time data for improved sustainability in the construction process. This research has identified fundamental factors leading systematically to the generation of concrete waste by creating an extensive dataset from construction firms all over Pakistan. This research study also identifies the potential concrete causes and proposed strategies towards the minimization of waste with a strong focus on the reuse and recycling of the same concrete material to enhance the adoption of sustainable practices. The prediction of the model indicates that the volumes of construction are to increase to 158 cubic meters by 2030 and 192 cubic meters by 2040. Further, it projects the increase in concrete construction waste volumes to 223 cubic meters by the year 2050 through historical wastage patterns.

Probabilistic Causal Network Modeling of Southern Hemisphere Jet Subseasonal to Seasonal Predictability

Abstract Skillful prediction of the Southern Hemisphere (SH) eddy-driven jet is crucial for representation of mid-to-high-latitude SH climate variability. In the austral spring-to-summer months, the jet and the stratospheric polar vortex variabilities are strongly coupled. Since the vortex is more predictable and influenced by long-lead drivers 1 month or more ahead, the stratosphere is considered a promising pathway for improving forecasts in the region on subseasonal to seasonal (S2S) time scales. However, a quantification of this predictability has been lacking, as most modeling studies address only one of the several interacting drivers at a time, while statistical analyses quantify association but not skill. This methodological gap is addressed through a knowledge-driven probabilistic causal network approach, quantified with seasonal ensemble hindcast data. The approach enables to quantify the jet’s long-range predictability arising from known late-winter drivers, namely, El Niño–Southern Oscillation (ENSO), Indian Ocean dipole (IOD), upward wave activity flux, and polar night jet oscillation, mediated by the vortex variability in spring. Network-based predictions confirm the vortex as determinant for skillful jet predictions, both for the jet’s poleward shift in late spring and its equatorward shift in early summer. ENSO, IOD, late-winter wave activity flux, and polar night jet oscillation only provide moderate prediction skill to the vortex. This points to early spring submonthly variability as important for determining the vortex state leading up to its breakdown, creating a predictability bottleneck for the jet. The method developed here offers a new avenue to quantify the predictability provided by multiple, interacting drivers on S2S time scales. Significance Statement Predictions of the Southern Hemisphere midlatitude jet stream are crucial for skillful forecasts of the austral mid-to-high latitudes. Several oceanic and atmospheric phenomena could, if better represented in models, improve spring-to-summer jet predictions on subseasonal to seasonal time scales. However, the combined potential skill arising from the inclusion of such phenomena has not been quantified. This study does so by using a probabilistic causal network model, representing the connections between those drivers and the jet with conditional probabilities, trained on large sets of model data. The stratospheric polar vortex is confirmed as crucial predictor of jet variability but is itself hard to predict a month in advance due to submonthly variability, creating a predictability bottleneck for the jet.

Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks

Indoor air quality prediction modeling for a naturally ventilated fitness building using RNN-LSTM artificial neural networks

Power Hardware-in-the-Loop Smart Inverter Testing with Distributed Energy Resource Management Systems

The increasing integration of grid-connected photovoltaic (PV) inverters and inverter-based resource (IBR) systems into the power grid emphasizes the critical need for standardized procedures to ensure their reliability and effective grid support functions. This research is driven by the gap in standardized testing methodologies for smart inverters, which are pivotal for the stability and quality of power in distributed energy systems. We used a Power Hardware-in-the-Loop (PHIL) laboratory setup to conduct a comprehensive analysis of smart inverters within a simulated real-world grid environment. Our approach integrates a Distributed Energy Resource Management System (DERMS) with PHIL testing to evaluate the smart inverter’s performance across various operational modes. The detailed test protocols mimic real-world grid conditions, enabling the examination of the inverter’s dynamic response to grid disturbances, control strategies, and communication protocols. The primary aim of this study is to rigorously test and validate the primary functions of smart inverters, focusing on their impact on overall grid stability and power quality management. This includes advanced features like Volt–VAR, Volt–Watt, dynamic power factor control, and the seamless integration of smart inverters into DERMSs and Advanced Distribution Management Systems (ADMSs). Furthermore, we aim to bridge the current gap in standardized testing procedures, contributing to the establishment of robust standards and operating protocols for smart inverter integration into the grid.

Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

Cultural Hybridity and Identity Formation in Globalized Societies

Purpose: The general purpose of the study was to explore cultural hybridity and identity formation in globalized societies. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to cultural hybridity and identity formation in globalized societies. Preliminary empirical review revealed that cultural hybridity was pervasive, with individuals blending diverse cultural influences to construct hybrid identities. Identity formation was dynamic and influenced by globalization, migration, and historical legacies. Power dynamics, including race and socio-economic status, intersected with cultural hybridity to shape experiences of identity and belonging. The study emphasized the importance of promoting inclusivity and dialogue to navigate the complexities of cultural hybridity and foster greater understanding across diverse cultural landscapes. Unique Contribution to Theory, Practice and Policy: The Post- Colonial theory, Intersectionality theory and Transnationalism theory may be used to anchor future studies on cultural hybridity and identity formation in globalized societies. The study provided valuable insights and recommendations that contributed to theory, practice, and policy. It enriched theoretical frameworks by adopting a multidimensional approach to understanding identity formation and underscored the importance of recognizing cultural diversity. Educators, community organizations, and policymakers utilized the study's findings to develop inclusive practices and policies that promoted intercultural understanding, social cohesion, and equitable access to resources. Additionally, media practitioners incorporated the study's insights to produce content that challenged stereotypes and fostered positive representations of cultural diversity. Overall, the study's recommendations have had a significant impact on efforts to promote inclusive development and intercultural dialogue in globalized societies.

The Psychological Effects of Environmental Degradation on Human Well-Being

Purpose: This study sought to investigate the psychological effects of environmental degradation on human well- being. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the psychological effects of environmental degradation on human well- being. Through a comprehensive review of empirical studies, it revealed the pervasive nature of environmental degradation's impact on human mental health, consistently finding associations between exposure to various environmental stressors and adverse psychological outcomes. The study emphasized the interconnectedness between environmental health and human well-being, highlighting the need for holistic approaches to sustainability that integrate mental health considerations. It underscored the importance of community engagement and participatory approaches in addressing the psychological effects of environmental degradation, advocating for community-based interventions to promote resilience and sustainable development. Unique Contribution to Theory, Practice and Policy: The Ecopsychology theory, Conservation Psychology theory and Stress and Coping theory may be used to anchor future studies on the psychological effects of environmental degradation on human well- being. The study offered valuable recommendations that contributed to theory, practice, and policy. It emphasized the need for an integrated theoretical framework that accounted for the multidimensional nature of environmental degradation and its psychological impacts. The recommendations highlighted the importance of integrating mental health considerations into environmental conservation efforts, promoting nature-based interventions, and prioritizing evidence-based policy reforms. Community engagement, interdisciplinary collaboration, and longitudinal research were identified as key strategies for addressing the psychological effects of environmental degradation and fostering resilience. Overall, the study provided a comprehensive roadmap for advancing understanding and addressing the complex interplay between environmental change and mental health outcomes.

Social Capital and Community Development

Purpose: This study sought to investigate the concept of social capital and its role in promoting community development and resilience, with a focus on practical interventions and policy implications. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to social capital and community development. In this study on social capital and community development, while all authors contribute to the overarching theme, one author stands out for discussing a slightly different topic. Specifically, the author who emphasizes the economic advantages of robust social capital within a community diverges somewhat from the others. While the study as a whole examines various aspects of community development, including economic prosperity, social cohesion, and resilience, this particular author delves deeper into the specific mechanisms through which social capital enhances economic opportunities, such as access to resources, markets, and employment. Therefore, this author's focus on the economic dimension of social capital provides a unique perspective within the broader context of community development discussed by the other authors. Unique Contribution to Theory, Practice and Policy: The Social Capital theory, Community Empowerment theory and Structural Functionalism theory may be used to anchor future studies on the concept of social capital and its role in promoting community development and resilience. The study made several recommendations that contributed to theory, practice, and policy in the field. It suggested further exploration of the mechanisms linking social capital to community development outcomes, integration of social capital considerations into policy frameworks, and implementation of community-based interventions. Additionally, the study emphasized the importance of capacity-building efforts, collaborative partnerships among stakeholders, and robust monitoring and evaluation mechanisms. By investing in these recommendations, stakeholders aimed to foster resilient, inclusive, and thriving communities, leveraging social capital as a catalyst for positive social change.

The Effectiveness of Inclusive Education Policies for Students with Disabilities

Purpose: This study sought to examine the effectiveness of inclusive education policies for students with disabilities. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the effectiveness of inclusive education policies for students with disabilities. Preliminary empirical review revealed that such policies played a vital role in promoting educational inclusion and academic success within mainstream educational settings. Despite encountering challenges such as inadequate resources and negative attitudes, inclusive education policies showed promise in creating equitable learning environments. The study emphasized the importance of addressing systemic barriers, promoting positive attitudes, and continuously monitoring and adapting policies to meet the evolving needs of students with disabilities. Overall, the findings underscored the potential of inclusive education policies to foster academic achievement and social inclusion for students with disabilities, contributing to a more equitable and supportive educational system. Unique Contribution to Theory, Practice and Policy: The Social Model of Disability, Ecological Systems theory and Critical Disability theory may be used to anchor future studies on inclusive education policies for students with disabilities. The study provided provided recommendations that enhanced theoretical frameworks, improved practice, and informed policy in inclusive education. It emphasized the importance of refining theoretical understandings of disability, providing professional development for educators, and developing comprehensive transition services for students with disabilities. Additionally, the study advocated for policy reforms that promote equity and social inclusion, fostered collaboration among stakeholders, and called for increased funding and support for inclusive education initiatives. These recommendations contributed to advancing inclusive education by promoting more responsive, equitable, and supportive educational environments for students with disabilities.

The Experiences of Refugees in Host Communities

Purpose: This study sought to examine the experiences of refugees in host communities. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to the experiences of refugees in host communities. Preliminary empirical review revealed that refugees encountered multifaceted challenges, including language barriers, discrimination, and limited access to education and employment opportunities, shaping their integration experiences. Positive host community attitudes and inclusive government policies were found to facilitate integration, while negative attitudes hindered social inclusion. Community support networks played a crucial role in fostering refugees' sense of belonging and social cohesion. Overall, the study highlighted the importance of a holistic and rights-based approach to refugee integration, emphasizing collaboration between stakeholders to promote empathy, understanding, and cooperation among host community members and refugees, thus fostering inclusive and resilient societies. Unique Contribution to Theory, Practice and Policy: The Social Identity theory, Acculturation theory and Ecological Systems theory may be used to anchor future studies the experiences of refugees in host communities. The study provided valuable recommendations that contributed to theory, practice, and policy in the field of refugee studies. It recommended further theoretical development to understand refugee experiences better and emphasized community-based initiatives for fostering positive interactions between refugees and host community members. Additionally, the study called for comprehensive integration policies, targeted interventions for vulnerable subgroups, and research-informed policymaking and practice. These recommendations aimed to create inclusive environments and promote the long-term integration and well-being of refugees in host communities.

Healthcare Disparities among Socioeconomic Groups

Purpose: This study sought to explore the socioeconomic factors contributing to disparities in healthcare access and outcomes. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to healthcare disparities among socioeconomic groups. Preliminary empirical review revealed that individuals from lower socioeconomic backgrounds faced multiple barriers to healthcare access, leading to inequalities in health outcomes. The study highlighted the complex interplay between social factors such as income, education, and wealth, emphasizing the need for comprehensive, multisectoral approaches to address healthcare disparities. Despite progress, gaps in research, policy, and practice remained, underscoring the importance of continued efforts to achieve health equity through collaborative initiatives and evidence-based interventions. Unique Contribution to Theory, Practice and Policy: The Social Determinants of Health theory, Health Benefit model and Critical Medical Anthropology theory may be used to anchor future studies on healthcare disparities among socioeconomic groups. The study provided comprehensive recommendations to advance theory, practice, and policy. It contributed to theoretical frameworks by highlighting the complex interplay between socioeconomic factors and healthcare disparities. In practice, it emphasized the importance of patient-centered care and collaborative models to address social determinants of health. Policy recommendations focused on structural interventions to improve social and economic conditions and expand healthcare coverage. Capacity building efforts aimed to enhance skills and competencies, while improved data collection and monitoring systems were advocated for tracking progress. Finally, a research agenda prioritized understanding the mechanisms underlying disparities and evaluating intervention effectiveness. These recommendations aimed to promote health equity and ensure equitable access to healthcare services.