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Cognitive biases in osteopathic diagnosis: a mixed study among French osteopaths.
Diagnosis (Berl)
January 2025
Scientific and Osteopathic Research Department, Institut de Formation en Ostéopathie du Grand Avignon IFO-GA, Avignon, France.
Objectives: Although cognitive biases are one of the most frequent causes of diagnostic errors, their influence remains underestimated in allied health professions, especially in osteopathy. Yet, a part of osteopathic clinical reasoning and diagnosis rely on the practitioner's intuition and subjective haptic perceptions. The aim of this study is to highlight links between the cognitive biases perceived by the practitioner to understand cognitive patterns during osteopathic diagnosis, and to suggest debiasing strategies.
View Article and Find Full Text PDFPublic Health Discussions on Social Media: Evaluating Automated Sentiment Analysis Methods.
JMIR Form Res
January 2025
Department of Health Administration, The College of Health Professions, Central Michigan University, Mt Pleasant, MI, United States.
Article Synopsis
- Sentiment analysis is a key method for analyzing text, especially in social media research, where the choice between manual and automated methods is crucial.
- The study compared several sentiment analysis tools, including VADER, TEXT2DATA, LIWC-22, and ChatGPT 4.0, against manually coded sentiment scores from YouTube comments on the opioid crisis, assessing factors like ease of use and cost.
- Findings revealed that LIWC-22 excelled in identifying sentiment patterns, while VADER was best at classifying negative comments, but overall, automated tools showed only fair agreement with manual coding, with ChatGPT performing poorly.
Systematic review of international ethics knowledge in the speech-language pathology literature (1980-2022).
Int J Speech Lang Pathol
January 2025
Centre for Augmentative and Alternative Communication, Faculty of Humanities, University of Pretoria, Pretoria, South Africa.
Purpose: A systematic review was conducted to describe various viewpoints expressed in speech-language pathology literature over four decades concerning ethics knowledge in the profession.
Method: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were employed. Our sample search encompassed peer-reviewed journal articles sourced from electronic databases including MEDLINE, CINAHL, ERIC, MasterFILE Premier, E-Journals, Africa-Wide Information, and Academic Search Premier, spanning the period from 1980 to 2022.
"Walking with Dreams": The Categories of Career Decision-Making Self-Efficacy and Its Influence on Learning Engagement of Senior High School Students.
Behav Sci (Basel)
December 2024
Institute of Education, Xiamen University, Xiamen 361005, China.
Career decision-making self-efficacy is a key factor influencing high school students' ability to make informed choices. It is closely associated with their professional interests, learning engagement, and academic performance. This study aims to explore the latent categories of career decision-making self-efficacy among Chinese high school students and analyze the differences in learning engagement across students with different types of career decision-making self-efficacy.
View Article and Find Full Text PDFIn this article, the authors propose a repurposing of the concept of entrustment to help guide the use of artificial intelligence (AI) in health professions education (HPE). Entrustment can help identify and mitigate the risks of incorporating generative AI tools with limited transparency about their accuracy, source material, and disclosure of bias into HPE practice. With AI's growing role in education-related activities, like automated medical school application screening and feedback quality and content appraisal, there is a critical need for a trust-based approach to ensure these technologies are beneficial and safe.
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