Professionalism and clinical short answer question marking with machine learning.
Intern Med J
; 52(7): 1268-1271, 2022 07.
Article
in En
| MEDLINE
| ID: mdl-35879236
Machine learning may assist in medical student evaluation. This study involved scoring short answer questions administered at three centres. Bidirectional encoder representations from transformers were particularly effective for professionalism question scoring (accuracy ranging from 41.6% to 92.5%). In the scoring of 3-mark professionalism questions, as compared with clinical questions, machine learning had a lower classification accuracy (P < 0.05). The role of machine learning in medical professionalism evaluation warrants further investigation.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Students, Medical
/
Professionalism
Limits:
Humans
Language:
En
Journal:
Intern Med J
Year:
2022
Document type:
Article