Objective: The aims of the study were: (1) to clarify the definitions of “migrant” used in occupational health research; (2) to summarize migrant workers’ industry sectors, occupations and employment conditions; (3) to identify the occupational health and safety services available to migrant workers; (4) to summarize work-related health problems found among migrant workers; (5) to identify the methodological challenges to research into occupational health of migrant workers; and (6) to recommend improvements in migrant occupational health research. Methods: This position paper was prepared by researchers from several European countries and Australia, working within the EU COST Action OMEGA-NET. The paper drew on two recent systematic reviews on the occupational health of international migrant workers and other literature, and also identified uncertainties and gaps in the research literature. Migrants may, for example, be temporary or permanent, moving for specific jobs migrants or other reasons. Their ethnicity and language capabilities will affect their work opportunities. Results: The occupational health literature seldom adequately identifies the heterogeneity or characteristics of the migrant group being studied. Migrants tend to work in more physically and mentally demanding environments with higher exposures than native workers. Migrants tend to have an increased risk of physical and mental ill health, but less access to health care services. This has been demonstrated recently by high rates of COVID-19 and less access to health care. There have been a number of cross-sectional studies of migrant health but few long-term cohort studies were identified. Other study designs, such as registry-based studies, surveys and qualitative studies may complement cross-sectional studies. Mixed-methodology studies would be valuable in research on migrants’ occupational health. Language and lack of trust are barriers to migrant research participation. Conclusion: Targeted research, especially longitudinal, identifying how these economically important but often-vulnerable workers can be best assisted is needed. Researchers should identify the characteristics of the migrant workers that they are studying including visa/migration circumstances (temporary, permanent, undocumented), racial and ethnic characteristics, existing skills and language abilities.

Preparing nursing students to achieve informatics competencies is essential in today’s information-intensive healthcare delivery systems. This study aimed to provide hands-on informatics experience to nursing students and to identify the frequency and type of home care clients’ health problems, nursing interventions, and outcomes using a standardized nursing terminology, the Omaha System. Data were collected by 159 nursing students on home visits to 598 clients, who received 8657 interventions from students and faculty for 2267 problems, in addition to the services provided by the home care centers. Skin, neuromusculoskelatal function, personal care, nutrition, and urinary function were the most common problems. The most common intervention category was teaching, guidance, and counseling (47%), followed by treatments and procedures (22%), surveillance (22%), and case management (9%). Outcomes evaluation showed significant improvement in 97.5% of the identified problems. Students gained informatics experience in the use of a standardized nursing language in electronic health records, data management, and use of nursing data at the basic practice level. This study demonstrated that the Omaha System was a useful data collection tool for evaluating problems, interventions, and outcomes in home care and a positive teaching and learning tool for baccalaureate nursing education.

Artificial intelligence (AI)-driven chatbots hold promise for improving patient care and healthcare efficiency, but integrating equality, diversity, and inclusion (EDI) remains challenging. This discussion paper explores the potential for EDI-focused chatbots, emphasizing the need for ongoing assessment, diverse datasets, and collaboration among healthcare providers, technologists, and policymakers. While acknowledging current limitations such as algorithmic bias, the paper also emphasizes the potential of AI to support and extend human decision-making, particularly through real-time analytics and scalable patient support. Embedding EDI principles helps reduce bias, enhances fairness, and requires cross-disciplinary collaboration to ensure AI delivers equitable, inclusive healthcare for all.