Background: A socially accountable health professional education curriculum aims to produce fit-for-purpose graduates to work in areas of need. 'Fit-for-purpose' can be assessed by monitoring graduate practice attributes.
Aim: The aim of this article was to identify whether graduates of 'fit-for-purpose' programmes are socially accountable.
Setting: The setting for this project was all 37 district hospitals in the KwaZulu-Natal province in Durban, South Africa.
Methods: We surveyed healthcare professionals working at district hospitals in the KwaZulu-Natal province. We compared four social accountability indicators identified by the Training for Health Network Framework, comparing medical doctors educated at the Nelson R. Mandela School of Medicine (NRMSM) with medical doctors educated at other South African and non-South African medical schools. In addition, we explored medical doctors' characteristics and reasons for leaving or staying at district hospitals.
Results: The pursuit of specialisation or skills development were identified as reasons for leaving in the next 5 years. Although one-third of all medical doctors reported an intention to stay, graduates from non-South African schools remained working at a district hospital longer than graduates of NRMSM or other South African schools and they held a majority of leadership positions. Across all schools, graduates who worked at the district hospital longer than 5 years cited remaining close to family and enjoyment of the work and lifestyle as motivating factors.
Conclusion: Using a social accountability approach, this research assists in identifying areas of improvement in workforce development. Tracking what medical doctors do and where they work after graduation is important to ensure that medical schools are meeting their social accountability mandate to meet community needs.
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http://dx.doi.org/10.4102/phcfm.v11i1.1962 | DOI Listing |
Ann Intern Med
January 2025
American College of Physicians, Philadelphia, Pennsylvania.
Ann Intern Med
January 2025
American College of Physicians, Philadelphia, Pennsylvania.
Ann Intern Med
January 2025
Clinical Epidemiology and Research Center (CERC), Department of Biomedical Sciences, Humanitas University, and IRCCS Humanitas Research Hospital, Milan, Italy, and Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Allergology and Immunology, Berlin, Germany (H.J.S.).
Description: Artificial intelligence (AI) has been defined by the High-Level Expert Group on AI of the European Commission as "systems that display intelligent behaviour by analysing their environment and taking actions-with some degree of autonomy-to achieve specific goals." Artificial intelligence has the potential to support guideline planning, development and adaptation, reporting, implementation, impact evaluation, certification, and appraisal of recommendations, which we will refer to as "guideline enterprise." Considering this potential, as well as the lack of guidance for the use of AI in guidelines, the Guidelines International Network (GIN) proposes a set of principles for the development and use of AI tools or processes to support the health guideline enterprise.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Psychological Institute and Network Aging Research, Heidelberg University, Heidelberg, Germany.
Background: Immersive virtual reality (iVR) has emerged as a training method to prepare medical first responders (MFRs) for mass casualty incidents (MCIs) and disasters in a resource-efficient, flexible, and safe manner. However, systematic evaluations and validations of potential performance indicators for virtual MCI training are still lacking.
Objective: This study aimed to investigate whether different performance indicators based on visual attention, triage performance, and information transmission can be effectively extended to MCI training in iVR by testing if they can discriminate between different levels of expertise.
J Med Internet Res
January 2025
Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States.
Background: Uncertainty in the diagnosis of lung nodules is a challenge for both patients and physicians. Artificial intelligence (AI) systems are increasingly being integrated into medical imaging to assist diagnostic procedures. However, the accuracy of AI systems in identifying and measuring lung nodules on chest computed tomography (CT) scans remains unclear, which requires further evaluation.
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