International Journal for Equity in Health is hosting a cross-journal Collection in collaboration with the Journal of Health, Population and Nutrition.

This cross-journal Collection is seeking contributions showcasing research on the intersection of migration and maternal and child health and nutrition. Both forced and voluntary migration have become a global phenomenon with profound implications for mothers' and children’s health, nutrition, and overall well-being. This special edition will highlight diverse methodological approaches, including qualitative, quantitative, and mixed-methods studies, that contribute to a deeper understanding of health inequities among migrant populations and offer potential solutions to mitigate them.

This special issue aims to bridge gaps in the evidence base and promote research incorporating diverse disciplinary perspectives and methodological synergies. We welcome theoretical, review, and empirical contributions that enhance our understanding of the complex social, political, economic, and cultural determinants shaping the health of migrant women and children. In addition to the mentioned methodologies, several others can be incorporated to strengthen this research. Participatory and community-based methods, such as participatory action research (PAR), involve affected communities in all stages of the research process, ensuring relevance and applicability. Qualitative and ethnographic approaches, including in-depth interviews and focus groups, provide a deeper understanding of the cultural and social contexts influencing health. Longitudinal studies track health outcomes over time, offering insights into the long-term effects of migration. An intersectional approach considers how multiple identity factors intersect and affect health. Social network analysis explores the role of social connections in accessing healthcare and resources. Policy analysis methods assess how national and international policies impact migrant health, while big data and spatial analysis reveal trends across different regions and contexts. We encourage submissions that engage with affected communities, apply innovative methodological frameworks, and inform policy and practice.

Submissions may include, but are not limited to, the following areas:

Health disparities in migrant populations:

- Examining health inequities affecting migrant mothers and children.

- Identifying key determinants of health disparities in various migration contexts.

Nutritional challenges and interventions:

- Assessing the nutritional status of migrant mothers and children.

- Evaluating policies, interventions, and strategies aimed at addressing nutritional challenges among migrant populations.

Impact of migration policies:

- Analyzing how migration policies influence maternal and child health outcomes.

- Investigating the role of legal frameworks in shaping access to healthcare and nutrition for migrant populations.

Psychosocial well-being:

- Exploring the psychosocial effects of migration on maternal and child health.

- Assessing the role of social support systems in mitigating mental health challenges.

Community health and resilience:

- Investigating community-based approaches to improve the health and resilience of migrant populations.

- Understanding the role of cultural adaptation and integration in health outcomes.

Health systems and access to care:

- Examining how health systems address the needs of migrant mothers and children.

-Identifying innovative approaches to enhance healthcare accessibility and equity for migrant populations.

This Collection supports and amplifies research related to SDG 5, Gender Equality.

This collection is open for submissions from all authors on the condition that the manuscript falls within both the scope of the collection and the journal it is submitted to.

All submissions in this collection undergo the relevant journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief of the relevant journal. As an open access publication, participating journals levy an article processing fee (International Journal for Equity in Health, Journal of Health, Population and Nutrition). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief of the journal where the article is being submitted.

In recent years, the challenge of drug resistance in solid tumors has emerged as a significant barrier to effective cancer treatment, often leading to treatment failure and poor patient outcomes. As traditional therapeutic approaches struggle to overcome this hurdle, there is a growing interest in leveraging innovative machine learning techniques to enhance our understanding of drug resistance mechanisms and improve therapeutic strategies. This article collection aims to gather cutting-edge research that explores the intersection of machine learning and oncology, focusing on how advanced algorithms can be applied to predict drug resistance, optimize treatment plans, and ultimately foster personalized medicine approaches in cancer care. By fostering interdisciplinary collaboration and sharing novel insights, this collection seeks to pave the way for transformative advancements in the fight against cancer.

This article collection explores integrating advanced machine learning methodologies—including ensemble learning, deep learning, reinforcement learning, explainable AI, and quantum computing—in understanding and overcoming drug resistance in solid tumors. We invite contributions highlighting novel algorithms, examples of innovative machine learning technique implementation, and interdisciplinary approaches, ultimately leading to improved therapeutic outcomes and enhanced patient care in oncology.

opics of Interest

The collection welcomes original research, reviews, commentary and methodology articles on the following topics:

• Ensemble Learning Techniques;

• Applications in predicting drug resistance profiles;

• Comparative studies of ensemble methods versus traditional approaches;

• Deep Learning in Oncology;

• Neural network architectures for tumor characterization;

• Image analysis and interpretation for drug response prediction;

• Reinforcement Learning;

• Adaptive treatment strategies using reinforcement learning;

• Simulation models for drug resistance evolution;

• Explainable AI;

• Methods for interpreting machine learning models in clinical settings;

• The role of explainability in improving treatment outcomes;

• Quantum Computing Applications;

• Quantum algorithms for optimizing drug discovery processes;

• Examples of quantum-enhanced machine learning techniques in oncology.

All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.

This collection supports and amplifies research related to SDG 3: Good Health and Well-Being.