Objective: To systematically map nutrition content in medical curricula across all 23 medical schools in Australia and New Zealand accredited by the Australian Medical Council (AMC).
Methods: A cross-sectional content analysis was conducted. Learning outcomes for 20 AMC-accredited medical curricula were extracted from online repositories and directly from universities in February to April 2021. Nutrition relevant learning outcomes or equivalent learning objectives/graduate attributes were identified. Nutrition learning outcomes were analysed according to Bloom's revised taxonomy to determine whether outcomes met cognitive, psychomotor or affective domains and at what level.
Results: Of the total 23 AMC-accredited medical curricula, 20 medical schools had learning outcomes able to be sourced for analysis. A total of 186 nutrition learning outcomes were identified within 11 medical curricula. One medical school curriculum comprised 129 of 186 (69.4%) nutrition learning outcomes. The majority of outcomes (181, 97.3%) were in the cognitive domain of Bloom's revised taxonomy, predominantly at level 3 'applying' (90, 49.7%). The psychomotor domain contained five nutrition learning outcomes (5, 2.7%), while the affective domain contained none. New Zealand medical curricula (153, 82.3%) contained 4.6-fold more nutrition learning outcomes than Australian curricula (33, 17.7%). When comparing clinical and preclinical years across curricula, the proportion of learning outcomes in the psychomotor domain was 3.7-fold higher in clinical years (4.08%) versus preclinical years (1.15%).
Conclusion: There is wide variation across medical curricula regarding the number of nutrition learning outcomes. This may lead to varying competency of medical graduates to provide nutrition care in Australia and New Zealand.
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http://dx.doi.org/10.1136/bmjnph-2022-000522 | DOI Listing |
Sci Rep
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Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
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Department of Electrical Engineering, College of Engineering, Taif University, P.O. BOX 11099, 21944, Taif, Saudi Arabia.
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Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.
The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.
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