2 research outputs found
Review of \u3ci\u3eAll Indians Do Not Live in Teepees (or Casinos)\u3c/i\u3e by Catherine C. Robbins
No full textCatherine C. Robbins\u27s highly personal tour of contemporary Indian Country begins with a moving description of 2,000 sets of human remains being returned from Harvard University to the people of the Pecos Pueblo and their kin at Jemez in 1999. The book then degenerates into a long rant of pet peeves that annoy its author.
Robbins\u27s portrait of Indian casinos is not flattering (their glitziness spoils reservation vistas, she says). She doesn\u27t think Indians dignify themselves by lecturing whites about sovereignty. In Robbins\u27s view, Indians practicing their hunting and fishing rights under treaties bring an unwelcome din to the streams and woods. Put all of this together, and, according to Robbins, we have a new stereotype: the Casino Indian, wily, rich, fat, corrupt, ready to ruin neighborhoods. They, writes the author, have morphed from downtrodden, peaceful Indians to sovereignty- spouting bad neighbors. No Indian tribe or nation\u27s members should brag about sovereignty, writes Robbins, unless they are prepared to offer a full range of governmental services, including senior care
Research Advance of Causal Inference in Clinical Medicine: A Bibliometrics Analysis via Citespace
No full textGuoqiang Qin,1 Jianxiang Wei,1,2 Yuehong Sun,3 Wenwen Du1 1School of Management, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China; 2Library, Nanjing University of Posts and Telecommunications, Nanjing, People’s Republic of China; 3School of Mathematical Sciences, Nanjing Normal University, Nanjing, People’s Republic of ChinaCorrespondence: Jianxiang Wei, School of Management, Nanjing University of Posts and Telecommunications, Nanjing, 210003, People’s Republic of China, Email [email protected] Yuehong Sun, School of Mathematical Sciences, Nanjing Normal University, Nanjing, 210023, People’s Republic of China, Email [email protected]: Causal inference in clinical medicine provides scientific evidence for precision medicine and individualized treatment by revealing the true associations between interventions and health outcomes. This study aims to conduct a comprehensive bibliometric analysis to identify current research trends, primary themes, and future directions for the application of causal inference in clinical medicine.Methods: We conducted a literature search in the Web of Science database using causal inference and medical terminology as subject keywords, covering the period from January 1986 to December 2024. After screening, we obtained 4,316 documents for analysis. Utilizing CiteSpace to generate network diagrams, we analyzed data related to the number of publications, citation analysis, collaboration relationships, keyword co-occurrence, and highlighted terms to illustrate the knowledge map and collaboration network in this field.Results: Publications on medical causal inference shows a fluctuating growth trend over time. The United States was the top contributors to this field. Harvard University is the leading research institution. George David Smith is the most prolific author, Robbins JM is the most cited scholar. The major research hotspots concentrated in fields such as epidemiology, coronary heart disease and health. Notably, marginal structural models, counterfactual forecasting, and Mendelian randomization have consistently been key methodologies in research. The burstness of keywords reveals that big data, DNA methylation, and robust estimation are emerging research directions.Conclusion: In clinical research, counterfactual forecasting provides prospective guidance for optimizing clinical strategies; Mendelian randomization helps uncover potential therapeutic targets; and marginal structural models enhance the accuracy of causal effect estimation in clinical studies. The future integration of various data sources to improve causal inference methods is anticipated to enhance the sensitivity and specificity of trials, ultimately elucidating the complex mechanisms of diseases and drug effects. The literature retrieve strategy and the metrics of the tools adopted may have a certain impact on the results of this study.Keywords: causal inference, counterfactual, marginal structural model, Mendelian randomization, bibliometric
