Methodological Gaps Research Articles

Mental Health of Healthcare Workers during COVID-19 Pandemic: Systematic Review and Meta-analysis

Introduction Outbreaks of infectious diseases, such as COVID-19, can lead to psychological distress and symptoms of mental illness, especially among healthcare workers (HCWs) who are at high risk of contracting the infection. This current crisis, in particular, adversely affects mental health due to the rapid spread of the infection from person to person and the uncertainty underlying the treatment guidelines, preventative measures, and the expected duration of its prevalence, which could affect the psychological, emotional, and behavioral symptoms. The purpose of this systematic review and meta-analysis was to identify, evaluate, summarize and analyze the findings of all relevant individual studies conducted to assess mental health symptoms during the COVID-19 pandemic. Furthermore, it aimed to identify any gaps in the literature, which could identify the potential for future research. Methods This PRISMA systematic review and meta-analysis on SCOPUS, Web of Science, Google Scholar, PubMed, and Science Direct stated from 11th Feb, 2021 to 11th March, 2022. Following the search to identify relevant literature, one author in the article evaluated the studies in relation to the inclusion and exclusion criteria. The systematic review included 31 studies, the meta-analysis of anxiety prevalence analyzed 20 studies and the meta-analysis of severe anxiety prevalence assessed 13 studies. Results As per the results that were obtained, for HCW, the most prevalent mental health symptoms were sleep disturbance, depression and anxiety, with a prevalence level of 42.9%, 77.6% and 86.5%, respectively. As per the pooled analysis, anxiety prevalence was recorded as 49% (95%CI, 0.36- 0.62), while for severe anxiety, the number dropped to 8% (95%CI, 0.05–0.10). The highest pooled prevalence of anxiety was observed in Turkey at 60% (95%CI, 0.51- 0.70). Alternatively, the lowest pooled prevalence was observed in China, 36% (95%CI, 0.23–0.50) and India, 36% (95%CI, 0.13–0.62). Based on the review of the relevant articles, a few methodological gaps were identified (i.e., Population of the studies and countries). Conclusion This study’s review and meta-analysis provide relevant information pertaining to the mental health status of healthcare workers across the world in light of the COVID-19 pandemic. They experience and endure high levels of mental health symptoms, and thus, it is necessary to provide them with mental and psychological support in this context.

Effects of the social environment on movement-integrated habitat selection

BackgroundMovement links the distribution of habitats with the social environment of animals using those habitats. Despite the links between movement, habitat selection, and socioecology, their integration remains a challenge due to lack of shared vocabulary across fields, methodological gaps, and the implicit (rather than explicit) historical development of theory in the fields of social and spatial ecology. Given these challenges can be addressed, opportunity for further study will provide insight about the links between social, spatial, and movement ecology. Here, our objective was to disentangle the roles of habitat selection and social association as drivers of movement in caribou (Rangifer tarandus).MethodsTo accomplish our objective, we modelled the relationship between collective movement and selection of foraging habitats using socially informed integrated step selection function (iSSF). Using iSSF, we modelled the effect of social processes, i.e., nearest neighbour distance and social preference, and movement behaviour on patterns of habitat selection.ResultsBy unifying social network analysis with iSSF, we identified movement-dependent social association, where individuals took shorter steps in lichen habitat and foraged in close proximity to more familiar individuals.ConclusionsOur study demonstrates that social preference is context-dependent based on habitat selection and foraging behaviour. We therefore surmise that habitat selection and social association are drivers of collective movement, such that movement is the glue between habitat selection and social association. Here, we put these concepts into practice to demonstrate that movement is the glue connecting individual habitat selection to the social environment.

An improved CRISPR and CRISPR interference (CRISPRi) toolkit for engineering the model methanogenic archaeon Methanococcus maripaludis

BackgroundThe type II based CRISPR-Cas system remains restrictedly utilized in archaea, a featured domain of life that ranks parallelly with Bacteria and Eukaryotes. Methanococcus maripaludis, known for rapid growth and genetic tractability, serves as an exemplary model for studying archaeal biology and exploring CO2−based biotechnological applications. However, tools for controlled gene regulation remain deficient and CRISPR-Cas tools still need improved in this archaeon, limiting its application as an archaeal model cellular factory.ResultsThis study not only improved the CRISPR-Cas9 system for optimizing multiplex genome editing and CRISPR plasmid construction efficiencies but also pioneered an effective CRISPR interference (CRISPRi) system for controlled gene regulation in M. maripaludis. We developed two novel strategies for balanced expression of multiple sgRNAs, facilitating efficient multiplex genome editing. We also engineered a strain expressing Cas9 genomically, which simplified the CRISPR plasmid construction and facilitated more efficient genome modifications, including markerless and scarless gene knock-in. Importantly, we established a CRISPRi system using catalytic inactive dCas9, achieving up to 100-fold repression on target gene. Here, sgRNAs targeting near and downstream regions of the transcription start site and the 5′end ORF achieved the highest repression efficacy. Furthermore, we developed an inducible CRISPRi-dCas9 system based on TetR/tetO platform. This facilitated the inducible gene repression, especially for essential genes.ConclusionsTherefore, these advancements not only expand the toolkit for genetic manipulation but also bridge methodological gaps for controlled gene regulation, especially for essential genes, in M. maripaludis. The robust toolkit developed here paves the way for applying M. maripaludis as a vital model archaeal cell factory, facilitating fundamental biological studies and applied biotechnology development of archaea.

Modeling Linguistic Landscapes

Abstract Much work in Linguistic Landscapes approaches multilingual landscapes in qualitative terms; inferential statistical approaches are still underrepresented. The present paper adds to filling this methodological gap by investigating the Linguistic Landscapes of St Martin, a highly multilingual, eastern Caribbean island, divided into a formerly Dutch-colonized and French-colonized part. It does so by employing an inferential statistical method to study the nature of linguistic diversity on signs. Based on 372 and 373 signs respectively for each of the commercial districts of Philipsburg and Marigot, we quantitatively analyze the occurrences of languages on the different signs and statistically model the data by means of Multi-Label Classification (MLC; e.g. Rivolli & de Carvalho, 2018). The results show that both shopping districts are characterized by multilingual patterns of Linguistic Landscapes but with differences in their exact linguistic setups, most importantly the interplay and relationships between different languages, motivated by differences in their sociolinguistic setup.

Millennials' Insurance Product Purchase Intention in Malaysia: An Exploratory Study

This cross-sectional study was conducted to identify the factors influencing Malaysian millennials' purchasing intentions for an insurance product after post-COVID. This study applied the Stimulus-Organism-Response (SOR) model. In particular, it investigates the antecedents of perceived value, income protection, and financial security that led to Malaysian millennials' purchase intention of insurance products, to which similar studies received less attention. Judgmental sampling approaches were used to acquire the samples. The data was analysed using the Partial Least Square Structural Equation Modelling technique (PLS-SEM) followed by Necessary Condition Analysis (NCA). According to the PLS-SEM estimation, all hypotheses were statistically significant. The finding shows that income protection is the most critical factor influencing insurance product purchase intentions, followed by financial stability and perceived value. Meanwhile, NCA results also demonstrated that income protection is the most crucial must-have factor driving insurance product purchasing intentions. Perceived value has a greater impact than financial security and ranks as the second must-have factor contributing to purchase intention, followed by financial security. Lastly, NCA results suggest that tax savings are not a must-have factor in determining the perceived value of an insurance product. This study provides valuable insights and practical implications for industry and practitioners. Meanwhile, to fill a methodological gap, this study used PLS-SEM combined with the NCA to uncover the must-have factors contributing to Malaysian millennials' intention to purchase insurance products.

Multiple habitat graphs: how connectivity brings forth landscape ecological processes

PurposeHabitat connectivity is integral to current biodiversity science and conservation strategies. Originally, the connectivity concept stressed the role of individual movements for landscape-scale processes. Connectivity determines whether populations can survive in sub-optimal patches (i.e., source-sink effects), complete life cycles relying on different habitat types (i.e., landscape complementation), and benefit from supplementary resources distributed over the landscape (i.e., landscape supplementation). Although the past decades have witnessed major improvements in habitat connectivity modeling, most approaches have yet to consider the multiplicity of habitat types that a species can benefit from. Without doing so, connectivity analyses potentially fail to meet one of their fundamental purposes: revealing how complex individual movements lead to landscape-scale ecological processes.MethodsTo bridge this conceptual and methodological gap, we propose to include multiple habitat types in spatial graph models of habitat connectivity, where nodes traditionally represent a single habitat type. Multiple habitat graphs will improve how we model connectivity and related landscape ecological processes, and how they are impacted by land cover changes.ResultsIn three case studies, we use these graphs to model (i) source-sink effects, (ii) landscape supplementation, and (iii) complementation processes, in urban ecosystems, agricultural landscapes, and amphibian habitat networks, respectively. A new version of the Graphab open-source software implements the proposed approach.ConclusionMultiple habitat graphs help address crucial conservation challenges (e.g., urban sprawl, biological control, climate change) by representing more accurately the dynamics of populations, communities, and their interactions. Our approach thereby extends the ecologist’s toolbox and aims at fostering the alignment between landscape ecology theory and practice.

METAPLANTCODE: Harmonizing Plant Metabarcoding Pipelines in Europe

The METAPLANTCODE project is dedicated to advancing and optimizing pan-European case studies on metabarcoding. The project's objectives include providing best practice recommendations, optimizing analysis pipelines for species identification, and creating user-friendly reference databases. To accomplish these objectives, METAPLANTCODE will identify and address gaps in current methodologies, publish best practice documents on FAIR (Findable, Accessible, Interoperable, Reusable) data publishing for plant metabarcode data to GBIF (Global Biodiversity Information Facility) and the INSDC (International Nucleotide Sequence Database Collaboration), and implement ELIXIR-compatible multimodal deep learning (DL) models in novel tools for standalone metabarcoding analyses using various data sources. A significant focus of the project is enhancing species identification accuracy through GBIF records and metadata. This involves mapping regional, national, and international botanical taxonomic checklists, red lists, and floras to the Catalogue of Life (COL) via the COL ChecklistBank. Additionally, taxonomic and floristic literature will be semantically enriched with new entity recognition and relationship extraction modules, supporting the enhanced identification of species through domain-specific descriptive and phenotypic features. An interface will link taxonomic names to treatments, identify homonyms and synonyms, and facilitate the conversion and annotation of floras, red lists, and ecological treatments. All METAPLANTCODE products will adhere to FAIR standards by the project's end. The project emphasizes knowledge transfer from the outset, engaging with associated partners and stakeholders. Key stakeholders will be identified, priorities set, and communication channels established, monitored, and adjusted as necessary. Efforts to enhance stakeholder engagement, training, and outreach will ensure that plant metabarcoding becomes a routine standard for biodiversity monitoring in Europe and beyond. Deep Learning for Plant Metabarcoding Within the METAPLANTCODE project, our team is tasked with improving taxonomic precision by integrating deep learning on metabarcoding data and metadata. Previous studies have demonstrated the applicability of deep learning to non-plant barcoding data and its computational efficiency compared to traditional bioinformatics approaches (Flück et al. 2022). Deep Learning Models for Metabarcoding Data Our approach involves evaluating the efficacy of several deep learning models—such as Convolutional Neural Networks (CNN)(LeCun et al. 2015), Transformer models (Vaswani et al. 2017), Hyena (Poli et al. 2023), and Mamba architectures (Gu et al. 2023)—on plant barcoding datasets. Preliminary results will be presented, highlighting the application of these models and the proposed ensemble method (Mohammed and Kora 2023), which combines multiple barcode sequence representations and learning strategies. The ensemble approach, when integrated with classical machine learning models such as logistic regression and Support Vector Machines (SVM) (Noble 2006), is anticipated to offer improved precision and robustness compared to individual model applications (Fig. 1). Multimodal Refinement of Predictions In the subsequent phase, we aim to refine genetic sequence classifications by employing a multimodal strategy. This approach will integrate genetic information with traditional botanical knowledge. We will utilize biological interaction lists (e.g., species-species, species-habitat) provided by the METAPLANTCODE project to train a large language model (LLM) on relevant scientific literature. This LLM, specifically tailored for plant biodiversity, will incorporate metadata associated with genetic samples (including location, temporality, and climatic conditions). By merging embeddings of both metadata and genetic data, we aim to enhance the accuracy of taxonomic predictions (Fig. 2). Conclusion Through this research, we aim to develop an effective method for integrating genetic data with textual information from various sources. We anticipate that this approach will not only enhance plant metabarcoding but also be applicable to other barcoding fields, such as bacteria, fish, fungi, and more. Additionally, we expect this methodology to find broader applications in genomic research, providing valuable insights and improvements across diverse biological disciplines.

A conceptual framework for integrating volunteers in emergency response planning and optimization assisted by decision support systems

In disaster response, the overwhelming amount of time-sensitive information and response options, combined with the dynamic nature of disasters, makes decision-making challenging for emergency service providers. Furthermore, it is often not economically feasible for countries to maintain a large number of full-time emergency responders. As such, many countries rely heavily on volunteer emergency responders during major disasters. This means that the success of disaster response often hinges on the efficient use of this volunteer workforce. We propose a framework for a Decision Support System (DSS) designed to optimize the use of volunteers by emergency services. This framework includes the data management layer, integrating necessary inputs and information; the analytical layer, which serves as the system's processing core; the user interface layer; and the decision-making layer. We argue that, while significant academic focus has been on the analytical layer, practical implementation requires the integration of all four components. Additionally, we emphasize the need for coordination with a broad spectrum of stakeholders involved in data provision, decision-making, and resource deployment for operationalizing this DSS. We also explore and analyze existing methodologies for developing the analytical layers, the requirements of these models, and the current methodological gaps. The proposed framework establishes a clear roadmap for adopting emergency response approaches that are human-centric, but at the same time, effectively utilize advancements in modeling, optimization, machine learning, and data integration.

A comprehensive taxonomy for forest fire risk assessment: bridging methodological gaps and proposing future directions.

Forest fire risk assessment plays a crucial role in the environmental management of natural hazards, serving as a key tool in the prevention of forest fires and the protection of various species. As these risks continue to evolve with environmental changes, the pertinence of contemporary research in this field remains undiminished. This review constructs a comprehensive taxonomic framework for classifying the existing body of literature on forest fire risk assessment within forestry studies. The developed taxonomy categorizes existing studies into 8 primary categories and 23 subcategories, offering a structured perspective on the methodologies and focus areas prevalent in the domain. We categorize a sample of 170 articles to present recent trends and identify research gaps in forest fire risk assessment literature. The classification facilitates a critical evaluation of the current research landscape, identifying areas in need of further exploration. Particularly, our review identifies underrepresented methodologies such as optimization modeling and some advanced machine learning techniques, which present routes for future inquiry. Moreover, the review underscores the necessity for model development that is tailored to specific regional data sets but also adaptable to global data resources, striking a balance between local specificity and broad applicability. Emphasizing the dynamic nature of forest fire behavior, we advocate for models that integrate the burgeoning field of machine learning and multi-criteria decision analysis to refine predictive accuracy and operational effectiveness in fire risk assessment. This study highlights the great potential for new ideas in modeling techniques and emphasizes the need for increased collaboration among research communities to improve the effectiveness of assessing forest fire risks.

Sustainability Committee And Environmental, Social And Governance (ESG) : A Systematic Literature Review

The objective of this study is to provide a comprehensive systematic literature review (SLR) of the empirical studies on the impact of sustainability committee (SC) and SC’s characteristics on ESG issues. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, 34 papers published between 2017 and 2023 were selected from the Web of Science and Scopus databases. The majority of the existing literature explored this topic in a cross-country context, with a particular focus on stakeholder theory. It was further found that empirical studies extensively focused on the presence of SC in relation to ESG; while less emphasis was placed on the characteristics of SC. While SC size and independence are the most commonly studied characteristics, it is also essential for future studies to further examine how other SC characteristics, such as gender diversity, expertise, and education, can exert an influence on ESG matters. Notably, there is a significant methodological gap. In addition, limited studies adopt qualitative and/or mixed methods in examining this relationship. Finally, the paper provides future research opportunities related to SC and ESG issues and identifies gaps in knowledge.

Life cycle assessment of the sustainability of bridges: methodology, literature review and knowledge gaps

Life cycle assessment of the sustainability of bridges: methodology, literature review and knowledge gaps

Diagnosing the status and trend of research on traditional knowledge related to non-timber forest products as food

Forest communities have a rich heritage of gathering, producing, and using non-timber forest products (NTFPs) as food. They have maintained and transferred traditional knowledge related to NTFPs through their networks over generations. Considering the food value of NTFPs, traditional knowledge has become an important social asset, and its functions in forest communities are clearly identifiable. This study systematically analyzed the research on traditional knowledge related to NTFPs as food—to demonstrate the status and trends of research in this regard—using a systematic review method and topic modeling. We found that studies predominantly focused on medicinal plants from Asian and African countries and their effect. Through topic modeling, research keywords were classified into six topics—namely, ethnomedicinal practices, biodiversity, measurement, community and food security, traditionality, and culture. This study's findings indicate three key messages—specifically, the dominance of traditional knowledge related to NTFPs as medicinal foods, a methodological gap in traditional knowledge research, and the contribution of ecosystem services of NTFPs to community sustainability.

Rethinking Tuberculosis Morbidity Quantification: A Systematic Review and Critical Appraisal of TB Disability Weights in Cost-Effectiveness Analyses.

The disability-adjusted life year (DALY), a key metric for health resource allocation, encompasses morbidity through disability weights. Widely used in tuberculosis cost-effectiveness analysis (CEAs), DALYs play a significant role in informing intervention adopt/reject decisions. This study reviews the values and consistency of disability weights applied in tuberculosis-related CEAs. We conducted a systematic review using the Tufts CEA database, updated to July 2023 with searches in Embase, Scopus and PubMed. Eligible studies needed to have included a cost-per-DALY ratio, and additionally either evaluated a tuberculosis(TB) intervention or included tuberculosis-related weights. We considered all tuberculosis health states: with/without human immunodeficiency virus (HIV) coinfection, TB treatments and treatment side effects. Data were screened and extracted independently by combinations of two authors. A total of 105 studies spanning 2002-2023 across 50 countries (mainly low- and middle-income countries) were extracted. Disability weights were sourced primarily from the Global Burden of Disease (GBD; 100/165; 61%), with 17 non-GBD studies additionally referenced, along with primary derivation. Inconsistencies in the utilisation of weights were evident: of the 100 usages of GBD-sourced weights, only in 47 instances (47%) had the weight value been explicitly specified with an appropriate up-to-date reference cited (constituting 28% of all weight usages, 47/165). Sensitivity analyses on weight values had been conducted in 30% of studies (31/105). Twelve studies did not clearly specify weights or their sources; nine further calculated DALYs without morbidity. The review suggests methodological gaps in current approaches for representing important aspects of TB, including TB-HIV coinfection, treatment, drug-resistance, extrapulmonary TB and psychological impacts. We propose a set of best practice recommendations. There is a need for increased rigour in the application, sensitivity testing and reporting of TB disability weights. Furthermore, there appears a desire among researchers to reflect elements of the tuberculosis experience beyond those allowed for by GBD disability weights.

The Intersection of Intellectual Property and Public Health

Purpose: The general objective of this study was to investigate the intersection of intellectual property and public health. 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 intersection of intellectual property and public health. Preliminary empirical review revealed that intellectual property laws, while designed to promote innovation, often created barriers to accessing essential medicines and technologies, particularly in low-income regions. It highlighted how patent protections, while incentivizing pharmaceutical development, led to high drug prices that limited access and exacerbated health disparities. The study emphasized the need for a balanced approach to IP regulations that would encourage innovation while ensuring that new treatments are affordable and accessible. It called for a re-evaluation of IP frameworks to align better with public health objectives and promote global health equity. Unique Contribution to Theory, Practice and Policy: The Theory of Intellectual Property as a Public Good, Theory of Technological Determinism and the Theory of Access to Medicines and Health Equity may be used to anchor future studies on the intersection of intellectual property and public health. The study recommended several key actions to address the issues identified. It suggested revising IP policies to include more flexibility, such as compulsory licensing and support for generic drug production, to improve access to essential medicines. The study advocated for international collaboration to balance IP protections with health needs and proposed that policymakers craft regulations that support both innovation and accessibility. It also emphasized the importance of ongoing research and evidence-based decision-making to guide IP reforms and promote global health equity. Additionally, it recommended fostering partnerships between governments, international organizations, and pharmaceutical companies to enhance healthcare access and address disparities.

Intellectual Property Rights in the Era of Artificial Intelligence

Purpose: The general objective of this study was to explore Intellectual Property Rights in the era of Artificial Intelligence. 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 Intellectual Property Rights in the era of Artificial Intelligence. Preliminary empirical review revealed that the era of Artificial Intelligence (AI) has significantly transformed the landscape of Intellectual Property Rights (IPR), presenting both opportunities and challenges. It highlighted that traditional IP laws are increasingly inadequate to address the complexities introduced by AI-generated content, necessitating a rethinking of existing frameworks. The study emphasized the need for recognizing AI's role in the creation of new works and inventions and the importance of developing balanced approaches to protect both human and AI contributions. Ethical considerations, such as accountability, transparency, and fairness, were also deemed crucial in ensuring responsible AI use. Overall, the study called for a comprehensive and proactive approach to integrate AI into IPR, ensuring robust protections while fostering innovation. Unique Contribution to Theory, Practice and Policy: The Technological Determinism Theory, Innovation Diffusion Theory and Legal Realism Theory may be used to anchor future studies on Intellectual Property Rights in the era of Artificial Intelligence. The study recommended revising existing IP laws to explicitly include AI-generated content and inventions, clarifying criteria for authorship and inventorship. It suggested expanding theoretical frameworks to accommodate AI contributions, emphasizing the collaborative nature of human and AI creativity. Practical measures, such as enhanced cybersecurity and legal safeguards for AI-generated trade secrets, were advised. Policy-wise, the study advocated for international cooperation to harmonize IP laws concerning AI. Developing ethical guidelines for responsible AI use and implementing education programs to inform stakeholders about AI and IP implications were also recommended. These measures aimed to create a balanced IP framework supporting innovation while protecting the rights of all stakeholders.

Evolving Environmental Regulations in Response to Climate Change

Purpose: The general objective of the study was to explore the evolving environmental regulations in response to climate change. 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 evolving environmental regulations in response to climate change. Preliminary empirical review revealed that that environmental regulations needed to be flexible and adaptive to remain effective amidst the rapidly changing climate landscape. It found that static regulatory frameworks were insufficient for addressing the complexities of climate change and highlighted the necessity for regulations to be updated regularly based on the latest scientific and technological advancements. Additionally, it emphasized that the success of these regulations depended on strong institutional capacities, effective enforcement, and international cooperation to ensure comprehensive and impactful responses to global climate challenges. Unique Contribution to Theory, Practice and Policy: The Theory of Environmental Regulation and Policy Change, Theory of Ecological Modernization and Theory of Policy Feedback may be used to anchor future studies on evolving environmental regulations in response to climate change. The study recommended that environmental regulations be designed with built-in flexibility to adapt to new scientific knowledge and technological advancements. It suggested enhancing institutional capacities by improving funding, training, and enforcement mechanisms. The study advocated for increased collaboration among stakeholders and greater international cooperation to create a unified regulatory framework. It also emphasized the importance of integrating economic and social considerations into regulatory design, supporting innovation, and ensuring transparency and inclusivity in policy-making processes. These recommendations aimed to improve the effectiveness of climate regulations and foster a more comprehensive and collaborative approach to addressing climate change.

Human Rights in the Context of Global Migration

Purpose: The general objective of the study was to explore human rights in the context of global migration. 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 human rights in the context of global migration. Preliminary empirical review revealed that global migration is deeply intertwined with significant human rights issues that vary across different regions and migrant populations. Migrants, regardless of their reasons for moving or legal status, frequently faced exploitation, discrimination, inadequate access to essential services, and social exclusion. The study emphasized that these human rights violations were often systemic and influenced by broader socio-political and economic structures. It highlighted the pervasive nature of rights violations at multiple stages of the migration process and pointed out substantial gaps in the implementation and enforcement of international frameworks designed to protect migrants. Specific groups, such as women, children, and undocumented migrants, were identified as particularly vulnerable, necessitating tailored interventions. The study argued that protecting migrants' human rights was both a moral and pragmatic necessity for fostering inclusive societies and called for a renewed commitment from all stakeholders to work collaboratively towards this goal. Unique Contribution to Theory, Practice and Policy: The Human Rights Theory, Push- Pull Theory of Migration and Social Capital Theory may be used to anchor future studies on global migration. The study recommended further development of migration and human rights theories to account for the complexities of contemporary migration flows, suggesting interdisciplinary approaches to enhance theoretical frameworks. Practically, it underscored the need for targeted support services for vulnerable migrant groups, the creation of safe migration pathways, and the establishment of specialized services addressing their unique needs. Policy recommendations included the adoption of comprehensive national and international policies that uphold the rights of all migrants, the enforcement of anti-discrimination laws, and the establishment of independent monitoring bodies. The study advocated for enhanced international cooperation to address migration challenges and suggested investing in data collection and research for informed policy-making. It also recommended promoting public awareness and education campaigns to foster inclusive attitudes towards migrants and counteract negative stereotypes, aiming to create a more just and humane global migration system.

The Impact of Data Privacy Laws on Digital Marketing Practices

Purpose: The general study focused on investigating the impact of data privacy laws on digital marketing practices. 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 impact of data privacy laws on digital marketing practices. Preliminary empirical review revealed that privacy laws such as GDPR, CCPA, APPI, and LGPD significantly transformed digital marketing practices, necessitating a shift towards more ethical and transparent data handling. These regulations increased compliance costs and administrative burdens but also improved data security and consumer trust. Companies that proactively adapted to these regulations by investing in robust data protection measures experienced enhanced consumer engagement and brand loyalty. The study highlighted the critical role of privacy-enhancing technologies in facilitating compliance and maintaining effective marketing strategies. Overall, the impact of data privacy laws was profound, requiring ongoing adaptation and innovation in digital marketing practices to balance regulatory compliance with consumer trust. Unique Contribution to Theory, Practice and Policy: The Theory of Planned Behaviour (TPB), Institutional Theory and Technology Acceptance Model (ATM) may be used to anchor future studies on data privacy laws. The study recommended several strategies for advancing the understanding and implementation of data privacy laws in digital marketing. It suggested that future research should explore the relationship between data privacy regulations and consumer behavior to develop effective marketing strategies. Businesses were advised to adopt proactive compliance measures, invest in data protection technologies, and educate consumers about their privacy rights. Policymakers were encouraged to develop balanced regulations through stakeholder collaboration and to harmonize international data privacy standards. The study also emphasized the importance of promoting privacy-enhancing technologies through public-private partnerships and government incentives to safeguard consumer data while supporting marketing innovation.

The Evolution of Employment Law in the Gig Economy

Purpose: The general objective of the study was to explore the evolution of employment law in the gig economy. 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 evolution of employment law in the gig economy. Preliminary empirical review revealed that traditional employment laws were insufficient for addressing the unique needs of gig workers, who face significant gaps in legal protections and benefits. The rise of gig work exposed flaws in existing legal frameworks, which were designed for permanent, full-time employment and fail to accommodate the flexible and often precarious nature of gig jobs. It was determined that existing laws needed substantial reform to better protect gig workers while maintaining the flexibility that defines gig work, emphasizing the need for updated legal frameworks that reflect the realities of modern employment. Unique Contribution to Theory, Practice and Policy: The Theory of Precarious Work, Flexicurity Theory and the Labor Market Segmentation Theory may be used to anchor future studies on the evolution of employment law. The study recommended several key reforms to address the shortcomings in the legal framework for gig workers. It suggested developing hybrid employment models that combine traditional benefits with the flexibility of gig work, reforming legal classifications to better fit gig work patterns, and enhancing regulatory frameworks to be more adaptive to technological and market changes. The study also advocated for promoting worker representation, implementing monitoring and evaluation mechanisms to assess the impact of reforms, encouraging international collaboration for harmonized regulations, and launching education and awareness initiatives to inform gig workers and employers about their rights and responsibilities. These recommendations aimed to create a more equitable and effective labor market for gig workers.

Human Rights and the Refugee Crisis

Purpose: This study sought to explore human rights and the refugee crisis. 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 human rights and the refugee crisis. Preliminary empirical review revealed that significant gaps remained in the protection of refugee rights despite existing international frameworks and policies. It found that refugees frequently faced inadequate living conditions, limited access to essential services, and insufficient integration opportunities. The research also highlighted the growing impact of environmental factors, such as climate change, on displacement, revealing that current policies often failed to address these emerging challenges effectively. The study emphasized the need for a more integrated approach that combines immediate humanitarian relief with long-term strategies to enhance both protection and integration for refugees. Unique Contribution to Theory, Practice and Policy: The Human Rights Theory, Realist Theory in International Relations and the Constructivist Theory in International Relations may be used to anchor future studies on the refugee crisis. The study recommended several key actions to improve the response to the refugee crisis. It suggested enhancing international collaboration and coordination among various stakeholders to ensure more effective aid delivery. It also emphasized the need to incorporate environmental considerations into refugee policies and strengthen legal frameworks to better protect refugee rights. Additionally, the study advocated for improved access to essential services, community-based integration approaches, support for advocacy and capacity building, and long-term planning to ensure sustainable and effective responses to the refugee crisis.