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Moral conflict and moral distress in veterinarians: a mixed-methods approach.

Aust Vet J

January 2025

Centre for Wellbeing Science, Faculty of Education, The University of Melbourne, Carlton, Victoria, Australia.

Veterinary professionals are often confronted with moral conflicts from which moral distress can develop. Moral distress can lead to a cascade of deleterious processes and outcomes including emotional anguish, distress, reduced patient care, and attrition from both the workplace and workforce. The current study established a pilot measure for moral distress in Australian veterinary clinicians, as well as reporting additional sources of moral and ethical conflicts in veterinary practice.

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Background: Population-level mammography screening for early detection of breast cancer is a secondary prevention measure well-embedded in developed countries, and the implications for women's health are widely researched. From a public health perspective, efforts have focused on why mammography screening rates remain below the 70% screening rate required for effective population-level screening. From a sociological perspective, debates centre on whether 'informed choice' regarding screening exists for all women and the overemphasis on screening benefits, at the cost of not highlighting the potential harms.

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Per- and polyfluoroalkyl substances (PFAS) have gained significant global attention due to their extensive industrial use and harmful effects on various organisms. Among these, perfluoroalkyl acids (PFAAs) are well-studied, but their diverse precursors remain challenging to monitor. The Total Oxidizable Precursor (TOP) assay offers a powerful approach to converting these precursors into detectable PFAAs.

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Machine learning outperforms humans in microplastic characterization and reveals human labelling errors in FTIR data.

J Hazard Mater

December 2024

Discipline of Chemistry, The University of Newcastle, University Drive, Newcastle, New South Whales 2308, Australia; School of Chemistry, Monash University, Wellington Road, Melbourne, Victoria 3800, Australia. Electronic address:

Microplastics are ubiquitous and appear to be harmful, however, the full extent to which these inflict harm has not been fully elucidated. Analysing environmental sample data is challenging, as the complexity in real data makes both automated and manual analysis either unreliable or time-consuming. To address challenges, we explored a dense feed-forward neural network (DNN) for classifying Fourier transform infrared (FTIR) spectroscopic data.

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Background: Chronic diseases are the leading cause of mortality and morbidity worldwide. Much of this burden can be prevented by adopting healthy behaviours and reducing chronic disease risk factors. Settings-based approaches to address chronic disease risk factors are recommended globally.

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