Understanding the Role of Gender in Feedback and Evaluation

1. Understanding the Role of Gender in Feedback and Evaluation Pediatric Coaching Program November 9, 2015 Bonnie Maldonado, MD Senior Associate Dean for Faculty Development and Diversity

2. 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

3. Diversity Facts and Figures

4. The National Landscape Women in Academic Medicine Underrepresented Minorities in Academic Medicine Full Professors Full Professors 5% 21% 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

5. Diversity at Stanford Medicine (2014)

6. Diversity at Stanford Medicine (2014)

7. Understanding where biases come from

8. Despite our best intentions, we are all vulnerable to making cognitive errors when evaluating others (students, teachers, patients, research data…)

9. 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.

10. Who is a “Scientist”? Barman, CR. (1999). J. Science Teacher Education, 10(1), 43-54.

11. 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 & Social Psych, 74(6), 1464-1480

12. IAT Results in the General Population:Gender-Science

13. IAT Results in the General Population:Gender-Career

14. Studies of Bias in AcademiA

15. 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.” Trix, F, & Psenka, C. (2003). Discourse & Society, 14(2), 191-220.

16. 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. Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. (2012) PNAS.

17. The Language of Faculty Evaluations In an analysis of 14 million reviews on RateMyProfessor.com brilliant nice Jaschik S. (2015) Inside Higher Ed. Feb 9, Rate My Word Choice. http://benschmidt.org/profGender/#

18. brilliant

19. nice

20. 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” • 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

21. Cross-GenderCoaching 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 Blake-Beard S, Bayne ML, Crosby FJ, Muller CB. (2011) J of Social Issues. 67(3): 622-643.

22. Strategies to Overcome Implicit Bias in Evaluation

23. 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 self-correct 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.

24. 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.

25. 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.

26. 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). PersSoc Psych Bull, 34(10), 1332-1345.

27. 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.

28. Continue this Discussion! OFDD Trainings and Events

29. 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)

30. OFDD Career Development Opportunities with a Focus on Diversity • Augustus White Professionalism Award • McCormick-Gabilan Faculty Award • HCOE/OFDD Faculty Fellowship • McCormick Distinguished Lecture Series Awards and Fellowships • Stanford Leadership Development Program • Funding to attend AAMC Development Seminars • Lunchtime skills building workshops • All-day Team Science Retreat Professional Development • Women’s Faculty Networking • URM Faculty Networking Networking

31. Questions?