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Table 1 Demographic data for undergraduate medical students according to the usage of large language models (n = 1718)

From: A cross sectional investigation of ChatGPT-like large language models application among medical students in China

Variables

Overall,

N = 17181

Used large language models

p-value2

Yes, N = 593

No, N = 1125

Age, Median (IQR)

20.0 (19.0–21.0)

20.0 (19.0–21.0)

20.0 (19.0–21.0)

0.07

Gender, n(%)

   

< 0.001

Female

898 (52.3)

222 (37.4)

676 (60.1)

 

Male

820 (47.7)

371 (62.6)

449 (39.9)

 

Year of study (level), n (%)

   

< 0.001

1st

31 (1.8)

6 (1.0)

25 (2.2)

 

2nd

541 (31.5)

220 (37.1)

321 (28.5)

 

3rd

831 (48.4)

284 (47.9)

547 (48.6)

 

4th

215 (12.5)

46 (7.8)

169 (15.0)

 

5th

100 (5.8)

37 (6.2)

63 (5.6)

 

Major (%)

   

0.01

Preventive medicine

301 (17.5)

114 (13.9)

187 (20.8)

 

Clinical medicine

1228 (71.5)

630 (76.8)

598 (66.6)

 

Public health management

73 (4.2)

29 (3.5)

44 (4.9)

 

Nursing

75 (4.4)

27 (3.3)

48 (5.3)

 

Basic medicine

41 (2.4)

20 (2.4)

21 (2.3)

 
  1. 1 IQR, interquartile range; Data were presented as n (%) and median (IQR)
  2. 2 Mann-Whitney U test; Chi-squared test