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Though notorious for its polluted air today, the city of Los Angeles once touted itself as a health resort. After the arrival of the transcontinental railroad in 1876, publicists launched a campaign to portray the city as the promised land, circulating countless stories of miraculous cures for the sick and debilitated. As more and more migrants poured in, however, a gap emerged between the city's glittering image and its dark reality. Emily K. Abel shows how the association of the disease with \"tramps\" during the 1880s and 1890s and Dust Bowl refugees during the 1930s provoked exclusionary measures against both groups. In addition, public health officials sought not only to restrict the entry of Mexicans (the majority of immigrants) during the 1920s but also to expel them during the 1930s. Abel's revealing account provides a critical lens through which to view both the contemporary debate about immigration and the U.S. response to the emergent global tuberculosis epidemic.

This is the first comprehensive account of \"Anatomy in National Socialism\". Traces the gradual escalation of ethical transgressions in anatomy during National Socialism from the traditional anatomical work with the dead to human experimentation, and points to the need for vigilance against similar gradual ethical compromise in contemporary medical ethics. Demonstrates the manner in which anatomists became complicit in the complete annihilation of the perceived \"enemies\" of the Nazi-government. Demands the full reconstruction of the biographies and memorialization of Nazi-victims, whose bodies were used for anatomical purposes.

Serious games are known as safe learning environments, allowing medical students to train their skills without endangering patients' safety. By integrating virtual patients via chatbots, serious games provide the opportunity to practice history taking. The study investigated the impact of self-directed learning by means of a customized guideline on history taking in two distinct chatbot systems embedded in serious games. Fourth-year medical students (  = 159) were randomized to one of two serious games, each representing an emergency department and simulating different clinical scenarios. Students played the serious games at two measurement points and received a guideline between both sessions. The chatbots differed in the manner of query entry, with one requiring students to formulate history taking questions themselves, while the other provided a long menu of selectable questions. The dependent variables analyzed included the history taking data entered into the chatbots, represented as a quantified history score, as well as students' comparative self-assessments of their learning outcomes. Comparing only the first measurement point, students achieved higher scores in the free-entry chatbot (85.2 ± 27.7) compared to the long menu chatbot (78.8 ± 35.7). Students achieved significantly higher scores in the second than in the first session in the long menu chatbot ( (315) = -2.918,  = .004,  = -0.229) but not in the free-entry chatbot after receiving the guideline. In terms of students' self-assessment, no significant difference between both serious games was found. The results suggest that history taking benefits from self-directed learning in a long menu format relying on cued recall but not in a free-entry chatbot relying on free recall. Since serious games are partially artificial learning environments for training history taking, future studies should examine the extent to which students can transfer their learning in and out of serious games.

Unlike any existing studies of the medical humanities,The Chief Concern of Medicinebrings to the examination of medical practices a thorough---and clearly articulated---exposition of the nature of narrative. The book builds on the work of linguistics, semiotics, narratology, and discourse theory and examines numerous literary works and narrative \"vignettes\" of medical problems, situations, and encounters. Throughout, the book presents usable expositions of the ways storytelling organizes itself to allow physicians and other healthcare workers (and even patients themselves) to be more attentive to and self-conscious about the information---the \"narrative knowledge\"---of the patient's story.

This book offers the first extended study published in English on the Hippocratic treatise On Regimen, one of the most important pre-Platonic documents of the discussion of human nature and other topics at the intersection of ancient medicine and philosophy.

Meticulously researched and beautifully written, Fit to Be Citizens? demonstrates how both science and public health shaped the meaning of race in the early twentieth century. Through a careful examination of the experiences of Mexican, Japanese, and Chinese immigrants in Los Angeles, Natalia Molina illustrates the many ways local health officials used complexly constructed concerns about public health to demean, diminish, discipline, and ultimately define racial groups. She shows how the racialization of Mexican Americans was not simply a matter of legal exclusion or labor exploitation, but rather that scientific discourses and public health practices played a key role in assigning negative racial characteristics to the group. The book skillfully moves beyond the binary oppositions that usually structure works in ethnic studies by deploying comparative and relational approaches that reveal the racialization of Mexican Americans as intimately associated with the relative historical and social positions of Asian Americans, African Americans, and whites. Its rich archival grounding provides a valuable history of public health in Los Angeles, living conditions among Mexican immigrants, and the ways in which regional racial categories influence national laws and practices. Molina's compelling study advances our understanding of the complexity of racial politics, attesting that racism is not static and that different groups can occupy different places in the racial order at different times.

Family health history (FHx) is an important predictor of a person's genetic risk but is not collected by many adults in the United States. This study aims to test and compare the usability, engagement, and report usefulness of 2 web-based methods to collect FHx. This mixed methods study compared FHx data collection using a flow-based chatbot (KIT; the curious interactive test) and a form-based method. KIT's design was optimized to reduce user burden. We recruited and randomized individuals from 2 crowdsourced platforms to 1 of the 2 FHx methods. All participants were asked to complete a questionnaire to assess the method's usability, the usefulness of a report summarizing their experience, user-desired chatbot enhancements, and general user experience. Engagement was studied using log data collected by the methods. We used qualitative findings from analyzing free-text comments to supplement the primary quantitative results. Participants randomized to KIT reported higher usability than those randomized to the form, with a mean System Usability Scale score of 80.2 versus 61.9 (P<.001), respectively. The engagement analysis reflected design differences in the onboarding process. KIT users spent less time entering FHx information and reported more conditions than form users (mean 5.90 vs 7.97 min; P=.04; and mean 7.8 vs 10.1 conditions; P=.04). Both KIT and form users somewhat agreed that the report was useful (Likert scale ratings of 4.08 and 4.29, respectively). Among desired enhancements, personalization was the highest-rated feature (188/205, 91.7% rated medium- to high-priority). Qualitative analyses revealed positive and negative characteristics of both KIT and the form-based method. Among respondents randomized to KIT, most indicated it was easy to use and navigate and that they could respond to and understand user prompts. Negative comments addressed KIT's personality, conversational pace, and ability to manage errors. For KIT and form respondents, qualitative results revealed common themes, including a desire for more information about conditions and a mutual appreciation for the multiple-choice button response format. Respondents also said they wanted to report health information beyond KIT's prompts (eg, personal health history) and for KIT to provide more personalized responses. We showed that KIT provided a usable way to collect FHx. We also identified design considerations to improve chatbot-based FHx data collection: First, the final report summarizing the FHx collection experience should be enhanced to provide more value for patients. Second, the onboarding chatbot prompt may impact data quality and should be carefully considered. Finally, we highlighted several areas that could be improved by moving from a flow-based chatbot to a large language model implementation strategy.

Background Clinical decision-making (CDM) refers to physicians’ ability to gather, evaluate, and interpret relevant diagnostic information. An integral component of CDM is the medical history conversation, traditionally practiced on real or simulated patients. In this study, we explored the potential of using Large Language Models (LLM) to simulate patient-doctor interactions and provide structured feedback. Methods We developed AI prompts to simulate patients with different symptoms, engaging in realistic medical history conversations. In our double-blind randomized design, the control group participated in simulated medical history conversations with AI patients (control group), while the intervention group, in addition to simulated conversations, also received AI-generated feedback on their performances (feedback group). We examined the influence of feedback based on their CDM performance, which was evaluated by two raters (ICC = 0.924) using the Clinical Reasoning Indicator – History Taking Inventory (CRI-HTI). The data was analyzed using an ANOVA for repeated measures. Results Our final sample included 21 medical students (age mean = 22.10 years, semester mean = 4, 14 females). At baseline, the feedback group (mean = 3.28 ± 0.09 [standard deviation]) and the control group (3.21 ± 0.08) achieved similar CRI-HTI scores, indicating successful randomization. After only four training sessions, the feedback group (3.60 ± 0.13) outperformed the control group (3.02 ± 0.12), F (1,18) = 4.44, p  = .049 with a strong effect size, partial η 2  = 0.198. Specifically, the feedback group showed improvements in the subdomains of CDM of creating context ( p  = .046) and securing information ( p  = .018), while their ability to focus questions did not improve significantly ( p  = .265). Conclusion The results suggest that AI-simulated medical history conversations can support CDM training, especially when combined with structured feedback. Such training format may serve as a cost-effective supplement to existing training methods, better preparing students for real medical history conversations.

Backgrounds Traditional methods of teaching history-taking in medical education are limited by scalability and resource intensity. This study aims to assess the effectiveness of simulated patient interactions based on a custom-designed Generative Pre-trained Transformer (GPT) model, developed using OpenAI’s ChatGPT GPTs platform, in enhancing medical students’ history-taking skills compared to traditional role-playing methods. Methods A total of 56 medical students were randomly assigned into two groups: an GPT group using GPT-simulated patients and a control group using traditional role-playing. Pre- and post-training assessments were conducted using a structured clinical examination to measure students’ abilities in history collection, clinical reasoning, communication skills, and professional behavior. Additionally, students’ evaluations of the educational effectiveness, satisfaction, and recommendation likelihood were assessed. Results The GPT-simulation group showed significantly higher post-training scores in the structured clinical examination compared to the control group (86.79 ± 5.46,73.64 ± 4.76, respectively, P  < 0.001). Students in the GPT group exhibited higher enthusiasm for learning, greater self-directed learning motivation, and better communication feedback abilities compared to the control group ( P  < 0.05). Additionally, the student satisfaction survey revealed that the GPT group rated higher on the diversity of diseases encountered, ease of use, and likelihood of recommending the training compared to the control group ( P  < 0.05). Conclusions GPT-based history-taking training effectively enhances medical students’ history-taking skills, providing a solid foundation for the application of artificial intelligence (AI) in medical education. Clinical trial number NCT06766383.

Among the most far-reaching effects of the modern environmental movement was the widespread acknowledgment that human beings were inescapably part of a larger ecosystem. With this book, Linda Nash gives us a wholly original and much longer history of \"ecological\" ideas of the body as that history unfolded in California's Central Valley. Taking us from nineteenth-century fears of miasmas and faith in wilderness cures to the recent era of chemical pollution and cancer clusters, Nash charts how Americans have connected their diseases to race and place as well as dirt and germs. In this account, the rise of germ theory and the pushing aside of an earlier environmental approach to illness constituted not a clear triumph of modern biomedicine but rather a brief period of modern amnesia. As Nash shows us, place-based accounts of illness re-emerged in the postwar decades, galvanizing environmental protest against smog and toxic chemicals. Carefully researched and richly conceptual, Inescapable Ecologies brings critically important insights to the histories of environment, culture, and public health, while offering a provocative commentary on the human relationship to the larger world.