Utilizing a mixed methods approach located between constructivist-interpretivist and critical-ideological research paradigms (Ponterotto, 2005), the current study builds upon previous research (Pace et al., 2006) that investigated the cultural validity of the Minnesota Multiphasic Personality Inventory (MMPI)-2 in its use with American Indians. Thirty items from MMPI-2 scales F, 1, 6, 8, and 9 were identified via item analysis as reflecting significant differences in endorsement rates between an American Indian sample and the MMPI-2 normative group. Semistructured interviews focused on these 30 items were conducted with 13 American Indian participants from an Eastern Woodlands Nation in Oklahoma. Interviews were audio recorded, transcribed, and then coded for themes using a qualitative coding analysis. Nine themes emerged: core belief system, experiences of racism and discrimination, conflicting epistemologies, living in two worlds, community connectedness, responsibility and accountability to the community, traditional knowledge, stories as traditional knowledge, and language and historic loss. Results of the current study demonstrate how the MMPI-2 may pathologize Indigenous worldviews, knowledge, beliefs, and behaviors rather than accurately assess psychopathology. Implications for practice and future research are addressed.
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http://dx.doi.org/10.1037/a0016110 | DOI Listing |
Background: The purpose of this study was to evaluate the performance and evolution of Chat Generative Pre-Trained Transformer (ChatGPT; OpenAI) as a resource for shoulder and elbow surgery information by assessing its accuracy on the American Academy of Orthopaedic Surgeons shoulder-elbow self-assessment questions. We hypothesized that both ChatGPT models would demonstrate proficiency and that there would be significant improvement with progressive iterations.
Materials And Methods: A total of 200 questions were selected from the 2019 and 2021 American Academy of Orthopaedic Surgeons shoulder-elbow self-assessment questions.
J Med Internet Res
January 2025
Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
Background: Neuroimaging segmentation is increasingly important for diagnosing and planning treatments for neurological diseases. Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFHepatology
January 2025
Hepatic Surgery Centre, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, People's Republic of China.
Background And Aims: Immune checkpoint inhibitors (ICIs) have revolutionized systemic hepatocellular carcinoma (HCC) treatment. Nevertheless, numerous patients are refractory to ICIs therapy. It is currently unknown whether diet therapies such as short-term starvation (STS) combined with ICIs can be used to treat HCC.
View Article and Find Full Text PDFNeuro Oncol
January 2025
Department of Medicine, Division of Experimental Medicine, McGill University.
Background: Glioblastoma is an aggressive brain cancer with a 5-year survival rate of 5-10%. Current therapeutic options are limited, due in part to drug exclusion by the blood-brain barrier, restricting access of targeted drugs to the tumor. The receptor for the type 1 insulin-like growth factor (IGF-1R) was identified as a therapeutic target in glioblastoma.
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