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http://dx.doi.org/10.1016/j.molmet.2014.11.002 | DOI Listing |
Open Mind (Camb)
January 2025
Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, CA, USA.
Is everyone equally justified in blaming another's moral transgression? Across five studies (four pre-registered; total = 1,316 American participants), we investigated the perception of -the appropriateness and legitimacy for someone to blame a moral wrongdoing. We propose and provide evidence for a moral commitment hypothesis-a blamer is perceived to have low moral standing to blame a moral transgressor if the blamer demonstrates weak commitment to that moral rule. As hypothesized, we found that when blamers did not have the chance or relevant experience to demonstrate good commitment to a moral rule, participants generally believed that they had high moral standing to blame.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Conservative Dental Sciences, College of Dentistry, Qassim University, Buraidah, Qassim, Saudi Arabia.
Purpose: The objective of this study was to explore the attitudes, practices, supports, and barriers of academic leaders regarding the use of Evidence-Based Health Professional Education (EBHPE).
Methods: A cross-sectional survey was conducted on 79 faculty members in leadership positions, from four different undergraduate colleges at Qassim University. A pre-validated questionnaire was distributed electronically.
Radiology
January 2025
From the Department of Radiology, Division of Musculoskeletal Radiology, NYU Grossman School of Medicine, 660 1st Ave, 3rd Fl, Rm 313, New York, NY 10016 (S.S.W., J.V., R.K., E.H.P., J.F.); Department for Diagnostic and Interventional Radiology, Eberhard Karls University Tübingen, University Hospital Tübingen, Tübingen, Germany (S.S.W.); Department of Radiology, University Hospital Basel, Basel, Switzerland (J.V.); Department of Radiology, Hospital do Coraçao, São Paulo, Brazil (T.C.R.); Academic Surgical Unit, South West London Elective Orthopaedic Centre (SWLEOC), London, United Kingdom (D.D.); Department of Radiology, Balgrist University Hospital, Zurich, Switzerland (B.F.); Department of Radiology, Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea (E.H.P.); Medscanlagos Radiology, Cabo Frio, Brazil (A.S.); Centre for Data Analytics, Bond University, Gold Coast, Australia (S.E.S.); Siemens Healthineers AG, Erlangen, Germany (I.B.); and Siemens Medical Solutions USA, Malvern, Pa (G.K.).
Background Deep learning (DL) methods can improve accelerated MRI but require validation against an independent reference standard to ensure robustness and accuracy. Purpose To validate the diagnostic performance of twofold-simultaneous-multislice (SMSx2) twofold-parallel-imaging (PIx2)-accelerated DL superresolution MRI in the knee against conventional SMSx2-PIx2-accelerated MRI using arthroscopy as the reference standard. Materials and Methods Adults with painful knee conditions were prospectively enrolled from December 2021 to October 2022.
View Article and Find Full Text PDFESC Heart Fail
January 2025
Division of Cardiology, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.
Aims: The study aims to examine characteristics and outcomes associated with health-related quality of life (HRQoL) in patients with heart failure (HF) with preserved, mildly reduced and reduced ejection fraction (EF) (HFpEF, HFmrEF and HFrEF).
Methods And Results: Data on HRQoL were collected in the Swedish Heart Failure Registry (SwedeHF; 2000-2021) using the EuroQoL 5-dimensional visual analogue scale (EQ 5D-vas). Baseline EQ 5D-vas scores were categorized as 'best' (76-100), 'good' (51-75), 'bad' (26-50) and 'worst' (0-25).
Sci Rep
January 2025
College of Policy Studies, Tsuda University, Tokyo, 151-0051, Japan.
As artificial intelligence (AI) technology is introduced into different areas of society, understanding people's willingness to accept AI decisions emerges as a critical scientific and societal issue. It is an open question whether people can accept the judgement of humans or AI in situations where they are unsure of their judgement, as in the trolley problem. Here, we focus on justified defection (non-cooperation with a bad person) in indirect reciprocity because it has been shown that people avoid judging justified defection as good or bad.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!