Understanding the Role of Gender in
Feedback and Evaluation
P EDIATRIC C OACHING P ROGRAM
N OVEMBER 9, 2015
Bonnie Maldonado, MD
Senior Associate Dean for Faculty Development and Diversity
Agenda
 Diversity Facts and Figures
 Understanding Where Bias Comes From
 Studies of Bias in Academia
 Strategies to Overcome Implicit Bias in
Evaluation
 Continue this Discussion! OFDD
Trainings and Events
2
DIVERSITY FACTS AND
FIGURES
3
The National
Landscape
Women in Academic
Medicine
Full
Professors
4
21%
Underrepresented Minorities
in Academic Medicine
Full
Professors
5%
34%
Associate Prof
7%
Associate Prof
44% Assistant
Professors
9% Assistant
Professors
43% Residents
9% Residents
51% US Population
30% US Population
Sources: AAMC–The State of Women in Academic Medicine, 2013-2014; AAMC
Faculty Roster, 2014; AAMC Diversity in Medical Education Report, 2012; U.S.
Census Bureau Estimates, 2010
Diversity at Stanford Medicine
(2014)
Diversity at Stanford Medicine
(2014)
UNDERSTANDING WHERE
BIASES COME FROM
7
Despite our best intentions, we are all
vulnerable to making cognitive errors
when evaluating others (students,
teachers, patients, research data…)
8
Where do implicit associations come from?
• Family, friends, co-workers, the media (e.g. TV,
books, Internet)
• Because biases come from the society in which
we live, people tend to share the same biases,
regardless of their gender or race.
9
Who is a “Scientist”?
Draw-A-Scientist Test: Percent of
Students Who Drew A Male Scientist
(N=1504)
100
90
80
73%
75%
70
60
%
58%
50
40
30
20
10
0
K-2nd grade (n=235) 3-5th grade (n=649) 6-8th grade (n=620)
10 Barman, CR. (1999). J. Science Teacher Education, 10(1), 43-54.
The Implicit Association
Test (IAT)
•
•
•
Developed by psychologists, this computer task
measures implicit bias.
Reaction times on the IAT indicate how much you
implicitly associate one concept (e.g. women) with
another (e.g. math).
Scores on the IAT correlate somewhat with
people’s explicit associations.
Greenwald, AG, McGhee, DE, & Schwartz, JLK (1998). J. Personality
11 & Social Psych, 74(6), 1464-1480
IAT Results in the General Population:
Gender-Science
12
IAT Results in the General Population:
Gender-Career
13
STUDIES OF BIAS IN
ACADEMIA
14
Letters of Recommendation for
Medical School Faculty
Analysis of 312 recommendation letters for 103 positions at a medical
school revealed different tendencies…
Letters for men:
Longer;
More references to CV,
Publications, Patients,
Colleagues
Letters for women:
Shorter;
More “doubt raisers” (hedges,
faint praise, and irrelevancies);
More references to personal life
“It’s amazing how much she’s
accomplished.”
15 Trix, F, & Psenka, C. (2003). Discourse & Society, 14(2), 191-220.
Evaluations in Academic Science
A nationwide sample of biology, chemistry, and physics professors (n=127)
evaluated application materials of an undergraduate science student
(female or male) for a lab manager position.
5.0
4.0
Male student
Female student
4.02
4.75
3.98
3.70
3.33
2.87
3.0
2.0
1.0
Competence
Hireability
Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ,
16 Handelsman J. (2012) PNAS.
Mentoring
The Language of
Faculty Evaluations
In an analysis of 14 million reviews on RateMyProfessor.com
brilliant
17
nice
Jaschik S. (2015) Inside Higher Ed. Feb 9, Rate My Word Choice.
http://benschmidt.org/profGender/#
brilliant
18
nice
19
Gendered Evaluation Language
Beyond the Academy
Stanford’s Clayman Institute for Gender Research analyzed
the language of hundreds of performance reviews from four
technology and professional-services firms
 Women receive 2.5 times the amount of feedback men
do about aggressive communication styles
 E.g., “Your speaking style is off-putting”


20
Women’s reviews had 2.4 times as many references to team
accomplishments over individual accomplishments
Women’s reviews had half as many references to technical
expertise
Silverman SE. (2015) Wall Street Journal. Sep 30, Rate Gender Bias at Work Turns Up in Feedback.
http://www.wsj.com/articles/gender-bias-at-work-turns-up-in-feedback-1443600759
•
Cross-Gender
Coaching and
Mentoring
Having a same-gender or same-race mentor is
often initially felt to be important for success
among STEM mentees
 Emotionally, this can help with early
•
relationship development and perceptions of
the guidance received
However, studies are inconsistent about the effect
of race and gender matching on actual
improvement in academic outcomes including
GPA (among students) and sense of efficacy or
confidence with “science fit”
 At times the opposite, generally no difference
21
Blake-Beard S, Bayne ML, Crosby FJ, Muller CB. (2011) J
of Social Issues. 67(3): 622-643.
Strategies to
Overcome Implicit Bias
in Evaluation
22
Promote Awareness in Self
and Others
•
Provide and seek out education about how implicit biases affect
decisions.
 Although implicit biases are difficult to change, people can learn to selfcorrect for them.
EXAMPLE OF EFFECTIVENESS
At one medical school, departments that participated in workshops on
unconscious bias had significantly higher odds (p<.05) of increasing the %
of women faculty hires.
Sheridan, JT et al. (2010). Acad Med 85(6), 999-1007.
23
Accountability for
Evaluation
•
Ask team members to explain reasons behind their
decisions.
 Biases can be attenuated when individuals are held accountable
for their evaluations.
EXAMPLE OF EFFECTIVENESS
Experiments have shown that accountability motivates
subjects to process social information in more analytic and
complex ways.
Tetlock, PE. (1985). Soc Psych Quarterly, 48(3), 227-236.
24
Assemble Diverse
Evaluators
•
Ensure diverse evaluation committees in order to ensure a
representation of diverse perspectives and backgrounds.
 When people of diverse backgrounds are involved in evaluation,
different viewpoints appear when examining a single candidates –
simply averaging individual scores can lead you astray!
EXAMPLE OF EFFECTIVENESS
A study of over 900 law firms found that having a female hiring partner
increases odds of a female associate hire by 13% (p<.05).
Gorman, E. (2005). American Sociological Review, 70(4), 702-728.
25
Ensure Enough Time is
Spent on Evaluation
•
Devote adequate time to evaluation.
 People are more likely to rely on stereotypes when distracted,
pressured, or relying solely on snap judgments.
EXAMPLE OF EFFECTIVENESS
In a series of experiments, automatic stereotyping was reduced
among participants when they made a specific intention to think
counter-stereotypic thoughts.
Stewart, BD, & Payne, BK. (2008). Pers Soc Psych Bull, 34(10), 13321345.
26
Prior to Evaluation: Make
Evaluation Criteria
Explicit
•
Decide on explicit criteria before conducting evaluations.
 Criteria tend to shift after the fact, in order to justify our original
decisions.
EXAMPLE OF EFFECTIVENESS
In a series of experiments, participants changed their criteria for
evaluation based on traditional gender stereotypes (i.e., a male police
chief, a female women’s studies professor).
Uhlmann, EL, & Cohen, G. (2005). Psych Science, 16(6), 474-480.
27
Continue this
Discussion! OFDD
Trainings and Events
28
Cultural Considerations in the
Biomedical Workplace
 Generational Conflict in the Workplace:
• Dr. Lydia Howell, UC Davis
• February 8, 2016, LK320
 Spirituality in Medicine
• Stanford Health Care Spiritual Care Team
• April 27, 2016, LK320
 Unconscious Bias in Academic Medicine (AY 15-16)
 Sexual and Gender Minorities in Medicine (AY 14-15)
 Cross-Cultural Patient Communication (AY 14-15)
 Microaggressions (AY 14-15)
29
OFDD Career Development Opportunities
with a Focus on Diversity
Awards and
Fellowships
 Augustus White Professionalism Award
 McCormick-Gabilan Faculty Award
 HCOE/OFDD Faculty Fellowship
 McCormick Distinguished Lecture Series
Professional
Development
 Stanford Leadership Development Program
 Funding to attend AAMC Development Seminars
 Lunchtime skills building workshops
 All-day Team Science Retreat
Networking
30
 Women’s Faculty Networking
 URM Faculty Networking
QUESTIONS?
31