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OBJECTIVES: This study investigates experiences of medical students across Canada related to consent for educational sensitive (i.e., pelvic, rectal) exams under anesthesia (EUAs). METHODS: A bilingual online questionnaire was developed and distributed to medical students across Canada. RESULTS: Of 134 respondents, 63% had performed a pelvic EUA, 35% a rectal EUA, and 11% another sensitive EUA during their training. For those who had performed pelvic EUA, 28% were unsure if consent had taken place, 26% reported no specific consent, 20% reported specific consent, and 25% had mixed experiences of consent. For rectal EUAs, 48% reported no specific consent, 37% were unsure if consent had taken place, 13% reported that there had been specific consent, and 2% reported mixed experiences. Most respondents were uncomfortable (36%) or not sure if they were comfortable (32%) with how the consent process was handled for student pelvic EUAs; 31% were comfortable. In open-ended responses, respondents described experiences related to variability, discomfort, and authority. CONCLUSIONS: Non-consensual educational sensitive EUAs continue to take place in medical training across Canada, although practices of consent are highly variable. The majority of respondents reported being uncomfortable or unsure if they were comfortable with how consent for educational sensitive EUAs was practised during their training, and some respondents struggled to express their discomfort given the power dynamics at play.
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This article identifies and examines a tension in mental health researchers' growing enthusiasm for the use of computational tools powered by advances in artificial intelligence and machine learning (AI/ML). Although there is increasing recognition of the value of participatory methods in science generally and in mental health research specifically, many AI/ML approaches, fueled by an ever-growing number of sensors collecting multimodal data, risk further distancing participants from research processes and rendering them as mere vectors or collections of data points. The imperatives of the "participatory turn" in mental health research may be at odds with the (often unquestioned) assumptions and data collection methods of AI/ML approaches. This article aims to show why this is a problem and how it might be addressed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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Inteligencia Artificial , Salud Mental , Humanos , Aprendizaje Automático , Emociones , Reconocimiento en PsicologíaRESUMEN
Abilify MyCite was granted regulatory approval in 2017, becoming the world's first "smart pill" that could digitally track whether patients had taken their medication. The new technology was introduced as one that had gained the support of patients and ethicists alike, and could contribute to solving the widespread and costly problem of patient nonadherence. Here, we offer an in-depth exploration of this narrative, through an examination of the origins and development of Abilify, the drug that would later become MyCite. This history illuminates how an antipsychotic can become a top-selling drug and maintain its blockbuster status for more than a decade. It also provides a detailed case study for how knowledge is constructed within the logic of biomedical capitalism, providing impetus to reexamine claims regarding how MyCite addresses patient nonadherence, engenders patient support, and is ethicist-approved.