The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
A review of anatomy education during and after the COVID‐19 pandemic: Revisiting traditional and modern methods to achieve future innovation
Open accessAbstract
The coronavirus disease 2019 (COVID‐19) pandemic has had enormous effects on anatomy education. During the pandemic, students have had no access to cadavers, which has been the principal way to learn anatomy since the 17th century. As it is difficult to predict future access to cadavers for students or in‐person classes, anatomy educators are encouraged to revisit all possible teaching methods in order to develop innovations. Here, we review anatomy education methods to apply to current and future education.
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Cites
Azuma, R. T. (1997) · Presence: Teleoperators and Virtual Environments · doi:10.1162/pres.1997.6.4.355 · Cited by 10,121
Coronavirus Disease 2019–COVID-19
Dhama, K., Khan, S., Tiwari, R., Sircar, S., Bhat, S., Malik, Y. S., Singh, K. P., Chaicumpa, W., Bonilla-Aldana, D. K., & Rodriguez-Morales, A. J. (2020) · Clinical Microbiology Reviews · doi:10.1128/cmr.00028-20 · Cited by 1,876
The effectiveness of virtual and augmented reality in health sciences and medical anatomy
Moro, C., Štromberga, Z., Raikos, A., & Stirling, A. (2017) · Anatomical Sciences Education · doi:10.1002/ase.1696 · Cited by 1,040
Teaching anatomy without cadavers
McLachlan, J. C., Bligh, J., Bradley, P., & Searle, J. (2004) · Medical Education · doi:10.1046/j.1365-2923.2004.01795.x · Cited by 602
Cadaveric dissection as an educational tool for anatomical sciences in the 21st century
Ghosh, S. K. (2017) · Anatomical Sciences Education · doi:10.1002/ase.1649 · Cited by 547
Chen, C.-M., & Wu, C.-H. (2015) · Computers & Education · doi:10.1016/j.compedu.2014.08.015 · Cited by 536
Extending body space in immersive virtual reality: a very long arm illusion
Kilteni, K., Normand, J.-M., Sanchez-Vives, M. V., & Slater, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0040867 · Cited by 499 · cited 2×
“One of the most interesting aspects of VR is that users can interact with the virtually generated environment (Kilteni et al., 2012; Izard et al., 2017).”
BackgroundLim, K. H. A., Loo, Z. Y., Goldie, S. J., Adams, J. W., & McMenamin, P. G. (2016) · Anatomical Sciences Education · doi:10.1002/ase.1573 · Cited by 494
Longhurst, G. J., Stone, D. M., Dulohery, K., Scully, D., Campbell, T., & Smith, C. F. (2020) · Anatomical Sciences Education · doi:10.1002/ase.1967 · Cited by 491
The human cadaver in the age of biomedical informatics
Aziz, M. A., Mckenzie, J. C., Wilson, J. S., Cowie, R. J., Ayeni, S. A., & Dunn, B. K. (2002) · The Anatomical Record · doi:10.1002/ar.10046 · Cited by 484
Cited by
Effectiveness of virtual reality in nursing education: a systematic review and meta-analysis
Liu, K., Zhang, W., Li, W., Wang, T., & Zheng, Y. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04662-x · Cited by 284
Metadata
- Authors
- Iwanaga, J., Loukas, M., Dumont, A. S., & Tubbs, R. S.
- Venue
- Clinical Anatomy
- Publisher
- Wiley
- Published
- 2020-08-24
- Volume
- 34
- Issue
- 1
- Pages
- 108-114
- ISSN
- 0897-3806 (ISSN-L)
- 1098-2353
- OpenAlex
- W3042293459
- Cited by
- 328
- References
- 66
- FWCI
- 15.92
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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