Table 2.

Word count, frequency of linguistic domain characteristics, and gender bias calculation of letters of recommendation by applicant gender and race/ethnicity

	Applicant gender			Applicant race/ethnicity
Letter characteristics	Female	Male	P value	URM	Non-URM	P value
Word count1	535.89	527.07	0.34	531.76	533.72	0.55
Grindstone domain2	0.15	0.17	0.15	0.14	0.16	0.72
Standout domain	0.51	0.53	0.95	0.36	0.52	< .01
Desirability domain	0.11	0.10	0.57	0.07	0.10	0.35
Research domain	1.26	1.27	0.99	1.07	1.27	0.26
Patient care domain	0.21	0.21	0.49	0.27	0.20	0.06
Skill/knowledge domain	0.06	0.08	0.49	0.08	0.07	0.41
Efficient/organized domain	0.23	0.26	0.30	0.22	0.25	0.69
Agentic personality Domain	0.24	0.22	0.45	0.27	0.23	0.10
Communal/friendly domain	0.31	0.33	0.28	0.37	0.32	0.12
Social/familial domain	0.09	0.09	0.98	0.08	0.09	0.39
Introverted domain	0.01	0.01	0.91	0.01	0.01	0.70
Gender bias3	−0.16	−0.18	0.15	−0.14	−0.17	0.28

URM = Underrepresented minority: self-reported as Black/African American and/or Hispanic/Latino

¹Word count excluding heading, salutation and signature. ²Numerical value for each domain represents the frequency with which terms contained in that particular domain appear within the letter of recommendation. Higher numbers represent increased use. ³Gender bias as calculated by http://slowe.github.io/genderbias/. Negative value represents male bias in language used. Higher absolute value corresponds with the strength of the bias