Context: The multiple mini-interview (MMI) is the primary admissions tool used to assess non-cognitive skills at Dundee Medical School. Although the MMI shows promise, more research is required to demonstrate its transferability and predictive validity, for instance, relative to other UK pre-admissions measures.
Methods: Applicants were selected for interview based on a combination of measures derived from the Universities and Colleges Admissions Service (UCAS) form (academic achievement, medical experience, non-academic achievement and references) and the UK Clinical Aptitude Test (UKCAT) in 2009 and 2010. Candidates were selected into medical school according to a weighted combination of the UKCAT, the UCAS form and MMI scores. Examination scores were matched for 140 and 128 first- and second-year students, respectively, who took the 2009 MMIs, and 150 first-year students who took the 2010 MMIs. Pearson's correlations were used to test the relationships between pre-admission variables, examination scores and demographic variables, namely gender and age. Statistically significant correlations were adjusted for range restrictions and were used to select variables for multiple linear regression analysis to predict examination scores.
Results: Statistically significant correlations ranged from 0.18 to 0.34 and 0.23 to 0.50 unrestricted. Multiple regression confirmed that MMIs remained the most consistent predictor of medical school assessments. No scores derived from the UCAS form correlated significantly with examination scores.
Conclusions: This study reports positive findings from the largest undergraduate sample to date. The MMI was the most consistent predictor of success in early years at medical school across two separate cohorts. UKCAT and UCAS forms showed minimal or no predictive ability. Further research in this area appears worthwhile, with longitudinal studies, replication of results from other medical schools and more detailed analysis of knowledge, skills and attitudinal outcome markers.
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http://dx.doi.org/10.1111/medu.12193 | DOI Listing |
Int J Drug Policy
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MGH Institute for Technology Assessment, Harvard Medical School, Boston, MA, 02144, USA. Electronic address:
The overdose epidemic in the United States is evolving, with a rise in stimulant (cocaine and/or methamphetamine)-only and opioid and stimulant-involved overdose deaths for reasons that remain unclear. We conducted interviews and group model building workshops in Massachusetts and South Dakota. Building on these data and extant research, we identified six dynamic hypotheses, explaining changes in stimulant-involved overdose trends, visualized using causal loop diagrams.
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Shandong University, No. 72, Binhai Road, Jimo, Qingdao City, Shandong Province, Qingdao, 266200, CHINA.
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Department of Urology, Peking University First Hospital, Beijing 100034, China.
Although considered an "eco-friendly" biodegradable plastic, polylactic acid (PLA) microplastic (PLA-MP) poses a growing concern for human health, yet its effects on male reproductive function remain underexplored. This study investigated the reproductive toxicity of PLA in male mice and its potential mechanisms. To this end, our in vivo and in vitro experiments demonstrated that after degradation in the digestive system, a significant number of PLA-MP-derived nanoparticles could penetrate the blood-testis barrier (BTB) and localize within the spermatogenic microenvironment.
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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.
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