Search

This article explores the use of AI-based assessments in employment and the legal and ethical considerations surrounding them. It emphasizes that these assessments must comply with anti-discrimination laws and guidelines. State and local governments have also enacted legislation to regulate their use. Professional groups have provided recommendations and guidance on the use of AI-based assessments. The article summarizes discussions from a conference on topics such as bias in machine learning algorithms, transparency in data collection, and concerns about privacy. It highlights the importance of collaboration between psychologists and other professions to ensure scientific integrity and protect human dignity in the workplace. [Extracted from the article]

Lucy Gracen,1 Melanie Aikebuse,2,3 Babak Sarraf,2– 4 Steven M McPhail,5 Anthony W Russell,6 James O’Beirne,7 Katharine M Irvine,8 Suzanne Williams,9 Patricia C Valery,4,* Elizabeth E Powell2– 4,* 1Department of Gastroenterology and Hepatology, Royal Brisbane and Women’s Hospital, Herston, QLD, Australia; 2Department of Gastroenterology and Hepatology, Princess Alexandra Hospital, Woolloongabba, QLD, Australia; 3Centre for Liver Disease Research, Faculty of Medicine, Translational Research Institute, The University of Queensland, Woolloongabba, QLD, Australia; 4QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; 5Australian Centre for Health Services Innovation School of Public Health, Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Brisbane, QLD, Australia; 6Endocrinology and Diabetes, the Alfred Hospital, Melbourne, VIC, Australia; 7Department of Gastroenterology and Hepatology, Sunshine Coast University Hospital, Birtinya, QLD, Australia; 8Mater Research, Translational Research Institute, Brisbane, QLD, Australia; 9Inala Primary Care General Practice, Inala, QLD, Australia*These authors contributed equally to this workCorrespondence: Elizabeth E Powell, Centre for Liver Disease Research, Faculty of Medicine, Translational, Research Institute, The University of Queensland, Level 5, West Wing, 37, Kent Street, Woolloongabba, Brisbane, QLD, 4102, Australia, Tel +61 412 014 337, Email e.powell@uq.edu.auBackground: Although clinical guidelines endorse screening for metabolic dysfunction-associated steatotic liver disease (MASLD) with advanced fibrosis in people with type 2 diabetes (T2D), the feasibility of and barriers and considerations relevant to implementing this approach in the community remain unclear.Methods: Sequential adults with T2D attending selected community clinics during 2021– 2023 were invited to receive a “liver health check” (n=543). A further 95 participants were referred directly from their general practitioner (GP) or self-referred to the study. A total of 302 participants underwent a point of care assessment of hepatic steatosis and stiffness (FibroScan) and were advised to see their GP to discuss the results. “Template” letters containing key results, their interpretation and advice about management of cardiometabolic risk, patient follow-up and referral criteria, were sent to participants’ GPs.Results: Referral to a tertiary liver clinic was advised in GP letters for 45 (15%) participants with an increased risk of clinically significant fibrosis (liver stiffness measurement ≥ 8), 15 participants with ‘red flags’ (eg splenomegaly, thrombocytopenia) and 2 with unsuccessful FibroScan examinations. A referral from GPs to the liver clinic was received for 27 (44%) of these 62 participants. Approximately 90% of GPs rated the “template” letters favourably on a Likert rating scale.Conclusion: The low rate of participation in the “liver health check” and liver clinic referral reflects a real-world scenario and may stem from societal under-recognition and engagement with MASLD, competing health priorities or under-appreciation of the link between liver fibrosis severity and mortality risk. Further studies need to address strategies to enhance participation in liver health assessments and determine their impact on liver-related morbidity/mortality and overall survival.Keywords: NAFLD, primary care, chronic liver disease, FibroScan

Abstract. -- This study reports the results of fieldwork conducted during June-August 2002 and February-May 2003 on a privately owned ranch in the southwestern Cross Timbers region of north central [...]

Magnetoresistance measurements were used to study magnetization reversal in permalloy wire junction structures.

Pakhi Sharma,1 Sanjeewa Kularatna,1 Bridget Abell,1 Karen Eagleson,2,3 Linh K Vo,1 Ureni Halahakone,1 Sameera Senanayake,1 Steven M McPhail1,4 1Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, Brisbane, QLD, Australia; 2Queensland Paediatric Cardiac Service, Queensland Children’s Hospital, Brisbane, QLD, Australia; 3Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia; 4Digital Health and Informatics Directorate, Metro South Health, Brisbane, QLD, AustraliaCorrespondence: Pakhi Sharma, Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove, Brisbane, QLD, 4059, Australia, Tel +61 401829045, Email p33.sharma@hdr.qut.edu.auAbstract: Neurodevelopmental disorders are a significant cause of morbidity. Early detection of neurodevelopmental delay is essential for timely diagnosis and intervention, and it is therefore important to understand the preferences of parents and clinicians for engaging with neurodevelopmental surveillance and follow-up care. Discrete choice experiment (DCE) may be an appropriate method for quantifying these preferences. This review systematically examined how DCEs have been designed and delivered in studies examining neurodevelopmental care of children and identified the preferred attributes that have been reported. PubMed, Embase, CINAHL, and Scopus databases were systematically searched. Studies were included if they used DCE to elicit preferences for a neurodevelopmental follow-up program for children. Two independent reviewers conducted the title and abstract and full-text screening. Risk of bias was assessed using a DCE-specific checklist. Findings were presented using a narrative synthesis. A total of 6618 records were identified and 16 papers were included. Orthogonal (n=5) and efficient (n=5) experimental designs were common. There was inconsistent reporting of design-related features. Analysis was primarily completed using mixed logit (n=6) or multinomial logit (n=3) models. Several key attributes for neurodevelopmental follow-up care were identified including social, behavioral and emotional support, therapy, waiting time, and out-of-pocket costs. DCE has been successfully used as a preference elicitation method for neurodevelopmental-related care. There is scope for improvement in the design and analysis of DCE in this field. Nonetheless, attributes identified in these studies are likely to be important considerations in the design and implementation of programs for neurodevelopmental care.Keywords: preferences, discrete choice experiment, attributes, neurodevelopment, follow-up