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Background One of the most pressing global threats to public health is the use of tobacco, which not only claims lives but also has significant negative social and economic impacts. Over 8 million individuals die globally each year as a result of using tobacco products. Children aged 13–15 years, are currently estimated to consume tobacco with a high rate of 8.4% in India, and 5.4% in the state of Gujarat. Almost half of the adolescents aged 13–15 year who consumed tobacco reported starting tobacco use at around 10 years of age. Preventing tobacco-related mortality is an urgent issue, globally. Objective This study aims to report the prevalence of tobacco consumption (TOCO) among 10–13-year-old school going slum children in the city of Gandhidham, which is the first such study in this population. Methodology Our study had a cross-sectional mixed method design and included 404 slum school going Early Adolescents (EAs) representing 3303 slum EAs. The subjects were recruited from 26 schools using multi-stage stratified random sampling technique with three tiers. A validated Global Youth Tobacco Survey (GYTS) questionnaire was used for students along with qualitative interviews of 17 teachers from the same schools between January and July 2024. Result This study revealed that 19.1% were Ever Tobacco Users (ETUs) while 6.2% were Current Tobacco Users (CTUs). Socio-environmental factors such as TOCO by parents, friends, siblings/cousins, teachers and those who purchased tobacco for family members significantly influence the consumption of tobacco. These findings were corroborated with the results from the qualitative interviews with teachers. Conclusion The high prevalence of tobacco use, coupled with disturbingly early initiation ages, highlights the importance of the future health of this vulnerable population. The fact that nearly one in five early adolescents has experimented with tobacco, and that initiation often occurs even before the age of 10, calls for a radical rethinking of tobacco prevention strategies. Traditional approaches that target older adolescents may be too late for this population, suggesting the need for prevention efforts to begin in early childhood.

Background Women in India’s informal economy face significant occupational health risks that remain largely undocumented and unaddressed. With limited labour protections and inadequate access to health services, informal women workers (IWWs) experience overlapping vulnerabilities related to gender, work conditions, and environmental exposures. This study explored the multi-level determinants of health among IWWs in Ahmedabad, India, to inform gender-responsive integration of occupational health within primary health care systems. Methods A qualitative study was conducted using focus group discussions (FGDs) guided by the Socioecological Model (SEM). Five FGDs were held with 41 women representing key occupational groups—agricultural workers, construction workers, street vendors, home-based workers, and waste recyclers. Discussions were recorded, transcribed, translated, and thematically analysed. Themes were organized across SEM domains: intrapersonal, interpersonal, organizational, community, and policy levels. Findings Participants reported multiple, intersecting health risks such as musculoskeletal disorders, respiratory problems, skin irritation, and heat-related illnesses. Psychological stress, economic insecurity, and work-family conflict were pervasive, compounded by gendered expectations and absence of social protection. Poor workplace infrastructure, including lack of sanitation and shade exacerbated illness and fatigue. Many women avoided drinking water due to lack of toilets, leading to dehydration and urinary problems. Health-seeking behaviour was shaped by trust and convenience; private clinics were preferred over public facilities despite higher costs. Awareness of government schemes such as Ayushman Bharat and e-Shram was limited. Participants expressed demand for pensions, maternity protection, and home-based livelihood support. Conclusion Findings underscore the urgent need for gender-responsive occupational health integration into primary health care system. The study informed a national policy roundtable that convened key stakeholders to co-develop actionable recommendations to improve occupational health coverage for women in India’s informal economy.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor–patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care. Comparative performance assessment of large language models identified ChatGPT-4 as the best-adapted model across a diverse set of clinical text summarization tasks, and it outperformed 10 medical experts in a reader study.

Ocular pain is a common complaint which forces the patient to seek immediate medical attention. It is the primeval first response of the body to any severe condition of the eye such as trauma, infections and inflammation. The pain can be due to conditions directly affecting the eye and ocular adnexa; or indirect which would manifest as referred pain from other organ structures such as the central nervous system. Paradoxically, there are several minor and non-sight threatening conditions, which also leads to ocular pain and does not merit urgent hospital visits. In this perspective, we intend to provide guidelines to the practising ophthalmologist for teleconsultation when a patient complains of pain with focus on how to differentiate the various diagnoses that can be managed over teleconsultation and those requiring emergency care in the clinic. These guidelines can decrease unnecessary hospital visits, which is the need of the hour in the pandemic era and also beyond. Patients who are under quarantine and those who are unable to travel would be benefitted, and at the same time, the burden of increased patient load in busy hospital systems can be reduced.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP), their effectiveness on a diverse range of clinical summarization tasks remains unproven. In this study, we apply adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Quantitative assessments with syntactic, semantic, and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with ten physicians evaluates summary completeness, correctness, and conciseness; in a majority of cases, summaries from our best adapted LLMs are either equivalent (45%) or superior (36%) compared to summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.

Engineering capstone projects are designed to provide students in their senior year an opportunity to apply the knowledge and experience they have gained during the undergraduate program. In the engineering field, communitybased capstone projects with local nonprofit organizations are rare as compared to traditional industry sponsored projects. Therefore, there exists an opportunity to establish greater number of partnerships between educational institutes and local nonprofits through senior capstone projects. This paper describes one such partnership established between Dunwoody College of Technology's Industrial Engineering Technology program and Second Harvest Heartland (SHH). The partnership consisted of pairing three Dunwoody students, as part of their capstone requirement, with SHH to study their food supply, demand, and distribution networks. SHH is one of the largest nonprofit food banks in the U.S. and is located in the greater Minneapolis-Saint Paul region. Through the reflections from this capstone project, this paper demonstrates the benefits of such community-based projects in addressing the needs of nonprofit organizations and increasing students' engagement with the community. The paper also discusses the extent to which such projects could help students attain the desired capstone course outcomes.

Engineering capstone projects are designed to provide students in their senior year an opportunity to apply the knowledge and experience they have gained during the undergraduate program. In the engineering field, community-based capstone projects with local nonprofit organizations are rare as compared to traditional industry sponsored projects. Therefore, there exists an opportunity to establish greater number of partnerships between educational institutes and local nonprofits through senior capstone projects. This paper describes one such partnership established between Dunwoody College of Technology's Industrial Engineering Technology program and Second Harvest Heartland (SHH). The partnership consisted of pairing three Dunwoody students, as part of their capstone requirement, with SHH to study food supply, demand and distribution networks. SHH is one of the largest nonprofit food banks in the U.S. and it is located in the greater Minneapolis-Saint Paul region. Through the reflections from this capstone project, this paper demonstrates the benefits of such community-based projects in addressing the needs of nonprofit organizations and increasing students' engagement with the community. The paper also discusses the extent to which such projects could help students attain the desired capstone course outcomes.