A systematic review of simulation-based training tools for technical and non-technical skills in ophthalmology
To evaluate all simulation models for ophthalmology technical and non-technical skills training and the strength of evidence to support their validity and effectiveness. A systematic search was performed using PubMed and Embase for studies published from inception to 01/07/2019. Studies were analysed according to the training modality: virtual reality; wet-lab; dry-lab models; e-learning. The educational impact of studies was evaluated using Messick's validity framework and McGaghie's model of translational outcomes for evaluating effectiveness. One hundred and thirty-one studies were included in this review, with 93 different simulators described. Fifty-three studies were based on virtual reality tools; 47 on wet-lab models; 26 on dry-lab models; 5 on e-learning. Only two studies provided evidence for all five sources of validity assessment. Models with the strongest validity evidence were the Eyesi Surgical, Eyesi Direct Ophthalmoscope and Eye Surgical Skills Assessment Test. Effectiveness ratings for simulator models were mostly limited to level 2 (contained effects) with the exception of the Sophocle vitreoretinal surgery simulator, which was shown at level 3 (downstream effects), and the Eyesi at level 5 (target effects) for cataract surgery. A wide range of models have been described but only the Eyesi has undergone comprehensive investigation. The main weakness is in the poor quality of study design, with a predominance of descriptive reports showing limited validity evidence and few studies investigating the effects of simulation training on patient outcomes. More robust research is needed to enable effective implementation of simulation tools into current training curriculums.
Errataetall: |
CommentIn: Eye (Lond). 2021 Jul;35(7):2051-2052. - PMID 32719523 |
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Medienart: |
E-Artikel |
Erscheinungsjahr: |
2020 |
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Erschienen: |
2020 |
Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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Enthalten in: |
Eye (London, England) - 34(2020), 10 vom: 23. Okt., Seite 1737-1759 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Lee, Roxanne [VerfasserIn] |
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Links: |
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Anmerkungen: |
Date Completed 18.06.2021 Date Revised 19.09.2023 published: Print-Electronic CommentIn: Eye (Lond). 2021 Jul;35(7):2051-2052. - PMID 32719523 Citation Status MEDLINE |
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doi: |
10.1038/s41433-020-0832-1 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM307895033 |
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520 | |a To evaluate all simulation models for ophthalmology technical and non-technical skills training and the strength of evidence to support their validity and effectiveness. A systematic search was performed using PubMed and Embase for studies published from inception to 01/07/2019. Studies were analysed according to the training modality: virtual reality; wet-lab; dry-lab models; e-learning. The educational impact of studies was evaluated using Messick's validity framework and McGaghie's model of translational outcomes for evaluating effectiveness. One hundred and thirty-one studies were included in this review, with 93 different simulators described. Fifty-three studies were based on virtual reality tools; 47 on wet-lab models; 26 on dry-lab models; 5 on e-learning. Only two studies provided evidence for all five sources of validity assessment. Models with the strongest validity evidence were the Eyesi Surgical, Eyesi Direct Ophthalmoscope and Eye Surgical Skills Assessment Test. Effectiveness ratings for simulator models were mostly limited to level 2 (contained effects) with the exception of the Sophocle vitreoretinal surgery simulator, which was shown at level 3 (downstream effects), and the Eyesi at level 5 (target effects) for cataract surgery. A wide range of models have been described but only the Eyesi has undergone comprehensive investigation. The main weakness is in the poor quality of study design, with a predominance of descriptive reports showing limited validity evidence and few studies investigating the effects of simulation training on patient outcomes. More robust research is needed to enable effective implementation of simulation tools into current training curriculums | ||
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