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Computer model for the cardiovascular system: development of an e-learning tool for teaching of medical students

Overview of attention for article published in BMC Medical Education, November 2017
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  • Above-average Attention Score compared to outputs of the same age (52nd percentile)
  • Average Attention Score compared to outputs of the same age and source

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103 Mendeley
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Title
Computer model for the cardiovascular system: development of an e-learning tool for teaching of medical students
Published in
BMC Medical Education, November 2017
DOI 10.1186/s12909-017-1058-1
Pubmed ID
Authors

David Roy Warriner, Martin Bayley, Yubing Shi, Patricia Victoria Lawford, Andrew Narracott, John Fenner

Abstract

This study combined themes in cardiovascular modelling, clinical cardiology and e-learning to create an on-line environment that would assist undergraduate medical students in understanding key physiological and pathophysiological processes in the cardiovascular system. An interactive on-line environment was developed incorporating a lumped-parameter mathematical model of the human cardiovascular system. The model outputs were used to characterise the progression of key disease processes and allowed students to classify disease severity with the aim of improving their understanding of abnormal physiology in a clinical context. Access to the on-line environment was offered to students at all stages of undergraduate training as an adjunct to routine lectures and tutorials in cardiac pathophysiology. Student feedback was collected on this novel on-line material in the course of routine audits of teaching delivery. Medical students, irrespective of their stage of undergraduate training, reported that they found the models and the environment interesting and a positive experience. After exposure to the environment, there was a statistically significant improvement in student performance on a series of 6 questions based on cardiovascular medicine, with a 33% and 22% increase in the number of questions answered correctly, p < 0.0001 and p < 0.001 respectively. Considerable improvement was found in students' knowledge and understanding during assessment after exposure to the e-learning environment. Opportunities exist for development of similar environments in other fields of medicine, refinement of the existing environment and further engagement with student cohorts. This work combines some exciting and developing fields in medical education, but routine adoption of these types of tool will be possible only with the engagement of all stake-holders, from educationalists, clinicians, modellers to, most importantly, medical students.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 103 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 16 16%
Researcher 14 14%
Student > Master 11 11%
Other 6 6%
Lecturer 6 6%
Other 18 17%
Unknown 32 31%
Readers by discipline Count As %
Medicine and Dentistry 27 26%
Nursing and Health Professions 9 9%
Social Sciences 5 5%
Psychology 4 4%
Engineering 4 4%
Other 16 16%
Unknown 38 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 November 2017.
All research outputs
#13,059,047
of 23,008,860 outputs
Outputs from BMC Medical Education
#1,545
of 3,365 outputs
Outputs of similar age
#202,571
of 437,733 outputs
Outputs of similar age from BMC Medical Education
#57
of 96 outputs
Altmetric has tracked 23,008,860 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,365 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 437,733 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.
We're also able to compare this research output to 96 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.