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Underserved communities face persistent challenges due to limited access to healthcare services. Digital volunteering offers opportunities for healthcare professionals to support these populations remotely. This study examined factors associated with healthcare workers’ intentions to participate in digital healthcare volunteering in Algeria. An extended technology acceptance model was used, incorporating perceived organizational support (OS), altruism, and social responsibility. A convenience sample of 142 healthcare workers completed a survey, and hierarchical regression analysis was conducted. Results indicated that perceived ease of use, social responsibility, altruism, perceived OS, and perceived usefulness were each significantly associated with intentions to engage in digital volunteering. The extended model explained 75.6% of the variance in intention, highlighting the relevance of psychological, organizational, and ethical factors. These findings provide insights for policymakers, healthcare organizations, and developers seeking to support digital volunteering initiatives. Limitations include the cross-sectional design and the use of convenience sampling, which may affect generalizability. Future research should consider longitudinal designs, larger and more diverse samples, and cross-cultural comparisons to validate and extend these findings.

Background/Objectives: Telemedicine offers significant potential to improve the quality and accessibility of geriatric care, particularly in resource-constrained settings. However, its effective implementation depends largely on healthcare professionals’ acceptance and willingness to use such systems. Drawing on an extended Technology Acceptance Model (TAM), this study examines the determinants of doctors’ and nurses’ intentions to adopt telemedicine for elderly care in Algeria, with particular emphasis on self-efficacy and institutional support. Methods: This cross-sectional study employed a structured questionnaire administered to 130 healthcare professionals, including physicians and nurses, in Algeria. Hierarchical multiple regression analysis was conducted to test the proposed hypotheses and assess the incremental explanatory power of the extended model. Results: The extended TAM accounted for 48.7% of the variance in intention to use telemedicine. Institutional support (β = 0.432, p < 0.001) and self-efficacy (β = 0.264, p = 0.001) emerged as the strongest predictors. Perceived ease of use (β = 0.178, p = 0.038) and perceived usefulness (β = 0.139, p = 0.021) also had significant positive effects. The inclusion of self-efficacy and institutional support increased the model’s explanatory power by 23.5%. Conclusions: The findings highlight the critical role of organizational support mechanisms, digital competencies, and system usability in fostering telemedicine adoption among healthcare professionals. The study provides practical implications for policymakers and healthcare institutions, emphasizing the need for targeted training programs, supportive infrastructure, and institutional policies that enhance confidence and facilitate the integration of telemedicine into clinical workflows.

Addressing the issues of water insecurity and unequal access to water has been a priority of the Sustainable Development Goals. Poor sanitation and inadequate access to drinking water are the second leading causes of death among children worldwide. Access to safe drinking water is a fundamental human right. Climate change represents one of public health’s greatest challenges and significantly exacerbates health disparities. According to the World Health Organization, approximately 3.6 billion people reside in areas highly vulnerable to the effects of climate change. Furthermore, climate change is anticipated to result in roughly 250,000 additional deaths each year between 2030 and 2050. In the United States, climate impacts such as severe storms and floods, escalating wildfires, extreme heat, poor air quality, and diminishing access to food and water disproportionately endanger Black, Brown, and Indigenous communities, low-income groups, people with disabilities, women, children, older adults, and others, making them more susceptible to the harmful health effects of climate change. The formulation and implementation of climate policies are essential to address the negative impacts of climate change. This chapter aims to: (a) describe the effects of climate change on access to safe drinking water; (b) emphasize the implications of climate change on drinking water disparities; (c) highlight policies to mitigate the effects of climate change; and d) discuss recommendations to tackle climate change and inequities in access to water. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

BACKGROUND: Maternal mortality in Brazil remains a critical indicator of social and racial inequalities, reflecting structural failures in access to and quality of obstetric care. Black women, particularly those categorized as black or brown, are at a higher risk of dying during pregnancy, childbirth, or the postpartum period. This is the result of the intersection of institutional racism, poverty, and social vulnerabilities. This study aimed to analyze trends and associated factors of maternal mortality among black women in Brazil from 2000 to 2020. METHODS: This is a retrospective cohort analytical study using data from the Brazilian Mortality Information System. The sample included women aged 10 to 49 years whose underlying cause of death was classified under ICD-10 codes O00-O99. Descriptive and bivariate analyses were conducted, as well as Poisson and multinomial logistic regressions to estimate adjusted risk ratios according to skin color, education, region, type, and place of death. RESULTS: A total of 40,907 maternal deaths were identified, with 59.2% occurring among black women. The maternal mortality ratio was 39% higher among black women compared to white women and more than double among Indigenous women. Low education, residence in the North and Northeast regions, deaths outside hospital settings, and lack of formal investigation were independently associated with increased risk. Direct obstetric causes accounted for most deaths, with hypertensive disorders and puerperal complications being the leading conditions. CONCLUSIONS: Maternal mortality among black women in Brazil reveals deep structural inequalities. Urgent public policies that incorporate an intersectional perspective, addressing race, gender, and class, are necessary to reduce disparities and ensure equitable and dignified maternal healthcare.

Background: Maternal mortality remains a major global health challenge, disproportionately affecting black and Indigenous women. Hypertensive disorders of pregnancy and postpartum hemorrhage are the leading direct causes of maternal death. Artificial intelligence (AI) tools have emerged as potential strategies for predicting these complications, yet concerns persist about their equity and validation across racial groups. Methods: A rapid review was conducted in five databases, PubMed, EMBASE, Web of Science, Scopus and LILACS, to synthesize recent evidence on the use of AI for preventing maternal mortality due to hypertension and postpartum hemorrhage. Studies published in the last five years that included racial or ethnic data were selected and analyzed narratively. Results: Ten studies met the inclusion criteria, showing high predictive accuracy of AI models (AUROC often >0.95) for severe maternal outcomes. However, few models incorporated racial variables or underwent external validation in racially diverse or low-resource populations. Evidence suggests that unrepresentative datasets may perpetuate or exacerbate existing health inequities. Conclusions: AI demonstrates strong technical performance in predicting maternal complications but limited equity in application. Broader racial representation, external validation, and ethical governance are essential for ensuring that AI-based tools reduce rather than reinforce racial disparities in maternal mortality.