The Effect of an Intervention to Break the Gender Bias Habit for Faculty at One Institution: A Cluster Randomized, Controlled Trial : Academic Medicine

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The Effect of an Intervention to Break the Gender Bias Habit for Faculty at One Institution

A Cluster Randomized, Controlled Trial

Carnes, Molly MD, MS; Devine, Patricia G. PhD; Baier Manwell, Linda MS; Byars-Winston, Angela PhD; Fine, Eve PhD; Ford, Cecilia E. PhD; Forscher, Patrick; Isaac, Carol PT, PhD; Kaatz, Anna PhD, MPH; Magua, Wairimu PhD; Palta, Mari PhD; Sheridan, Jennifer PhD

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doi: 10.1097/ACM.0000000000000552
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Nearly two decades ago, Fried and colleagues1 conducted one of the first series of interventions aimed at promoting gender equity in academic medicine. Interventions were structural (e.g., changing the time of department meetings) and benefited both men and women. In the nearly two decades since publication of this study, it has become clear that addressing structural issues alone, although important, is insufficient if we are to achieve gender equity in academic medicine, science, and engineering.2–4 Recognizing the complexities of professional development for women in academic medicine, Magrane and colleagues5 put forth a multifaceted conceptual model that situates women faculty members as agents within several interdependent, complex adaptive systems that include organizational influences, individual decisions, societal expectations, and gender bias. Westring and colleagues4 found in a comprehensive, multilevel assessment that four distinct but related dimensions contributed to a work environment conducive to women’s academic success: equal access, work–life balance, supportive leadership, and freedom from gender bias.

Reports from the Association of American Medical Colleges and the National Academies of Science conclude that gender bias operates in personal interactions, evaluative processes, and departmental cultures to subtly yet systematically impede women’s career advancement in academic medicine, science, and engineering.6–8 Experimental research confirms that this persistent gender bias is rooted in culturally ingrained gender stereotypes that depict women as less competent than men in historically male-dominated fields such as medicine, science, and engineering—particularly in leadership positions.6–11 Despite significant reductions in explicitly endorsed stereotype-based gender bias and adoption of antidiscriminatory policies over the past half century, subtle forms of gender bias that may be inadvertent and unintentional persist.6–12


There is growing evidence that stereotype-based bias functions like a habit as an ingrained pattern of thoughts and behaviors.13–15 Changing a habit is a multistep process. Successful habit-changing interventions not only increase awareness of problematic behavior but must motivate individuals to learn and deliberately practice new behaviors until they become habitual.16 Much research in this area emanates from studies of health behavior change.17 In the only study to approach unintentional race bias as a habit, Devine and colleagues13 were able to reduce automatic negative assumptions about blacks by undergraduate students who were randomly assigned to practice stereotype-reducing cognitive strategies compared with students in a control group. Building on that work, we approached gender bias by faculty in academic medicine, science, and engineering as a remediable habit. We hypothesized that strategies employed to help individuals break other unwanted habits would assist faculty in breaking the gender bias habit (Figure 1)13,14,16,18 and positively influence department climate.15,19–23

Figure 1:
Conceptual model underpinning a study of 46 experimental and 46 control departments, University of Wisconsin–Madison, 2010–2012. Multistep process for reducing faculty’s gender bias habit in academic medicine, science, and engineering.


Design overview

We conducted a pair-matched, single-blind, cluster randomized, controlled study at the University of Wisconsin–Madison (UW-Madison) comparing a gender bias habit-reducing intervention delivered separately to 46 departments with 46 control departments. The control departments were offered the intervention after its effects were assessed in the experimental departments (“wait-list controls”).24 Participants were faculty in these 92 medicine, science, or engineering departments. They were surveyed within two days before the intervention and at three days and again at three months post intervention. Participants were unaware of allocation status. The investigating team was blinded to department assignment until random allocation was complete. The UW-Madison institutional review board approved this study. Participants gave informed consent for each survey and at each workshop intervention.

Setting and participants


Eligible departments resided in the six schools/colleges that house most medicine, science, and engineering faculty at UW-Madison. We excluded the School of Nursing because it had no comparable unit for matching. Three large departments were separated into their existing divisions which function administratively like departments (e.g., subspecialties in the Department of Medicine). These divisions were thereafter designated “departments.” Two small subspecialties, formerly part of a larger department, were returned to the “base” department; two departments that cross two schools but have a single chair were merged; and one small basic science department was merged with a closely related basic science department in the same college. Two departments were excluded: One was used as a pilot to test measures and provide feedback during workshop development and one was excluded because the department chair was a study investigator. A total of 2,290 faculty in 92 departments participated in the study.


During the 12-month period beginning September 2009, a study investigator (M.C. or J.S.) attended a regularly scheduled department meeting to describe the study. One of the 92 departments declined this visit, and its members received only a handout. Attendees were told that their department would be invited to participate in a workshop sometime over the next two to three years and that they would receive an online survey before and twice after their department’s workshop. Because control departments would receive questionnaires unattached to a workshop, we stated that we would send a series of the online surveys at some point over the course of the study “to assess the reliability of our measures.” Department visits occurred before random allocation, so neither investigators nor participating faculty were aware of allocation status at these visits.25 Faculty were unaware that any randomization would occur.26

Pair matching.

We assigned each department one of four broad disciplinary codes: biological, physical, or social science; or arts and humanities. We paired departments on the basis of disciplinary category, school/college, and size to ensure balanced treatment allocation and distribution of characteristics that might affect outcome measures.27,28 After independently creating department matches for these factors, four of the study investigators met to attain consensus (L.B., M.C., E.F., J.S.). Because faculty gender and rank could not be evenly matched in control and experimental departments, those variables were included as covariates in data analysis.

Randomization and intervention

The departments within each pair were randomly allocated to experimental or control status using a random number generator. No deviations from random assignment occurred.25 All experimental departments were offered the workshop between September 2010 and March 2012; control departments were offered workshops 12 to 18 months after completion of data collection. One investigator (M.C.) delivered all or part of a standardized workshop format14 to each experimental department; a second investigator (P.D. or J.S.) copresented at 41 workshops.29

The intervention, a 2.5-hour interactive workshop, occurred at the department level to enhance participation, avoid group contamination,30 and capitalize on existing organizational networks.19,20 Incorporating principles of adult education31,32 and intentional behavioral change,16,18 we designed the workshop to first increase faculty’s awareness of gender bias in academic medicine, science, and engineering, and then to promote motivation, self-efficacy, and positive outcome expectations for habitually acting in ways consistent with gender equity (Figure 1).14 The workshop began by making the case for the importance of utilizing all available talent to advance science, improve the nation’s health, and promote economic vitality. We reviewed research on the pervasiveness of stereotype-based gender bias in decision making and judgment and its detrimental effect on these goals. Participants then identified how gender equity could enhance their own department or field. Three modules followed this introduction. The first reviewed research on the origins of bias as a habit.12,14 The second promoted “bias literacy”33 by describing and labeling six manifestations of stereotype-based gender bias relevant to academic settings:

  • expectancy bias (i.e., how group stereotypes lead to expectations about individual members of that group)34;
  • prescriptive gender norms (i.e., cultural assumptions about how men and women should and should not behave and the social penalties of violating these norms)35;
  • occupational role congruity (i.e., the subtle advantage accrued to men being evaluated for roles that require traits more strongly linked to male stereotypes such as scientist and leader)36;
  • redefining credentials (i.e., how the same credential can be valued differently depending on who has it)37;
  • stereotype priming (i.e., ways in which even subtle reminders of male or female gender stereotypes bias one’s subsequent judgment of an individual man or woman38,39; and
  • stereotype threat (i.e., how fear of confirming a group’s negative performance stereotype can lead a member of that group to underperform, such as girls in math or women in leadership).40

The third module promoted self-efficacy for overcoming gender bias by providing five evidence-based behavioral strategies to practice13,14:

  • stereotype replacement (e.g., if girls are being portrayed as bad at math, identify this as a gender stereotype and consciously replace it with accurate information)13;
  • positive counterstereotype imaging (e.g., before evaluating job applicants for a position traditionally held by men, imagine in detail an effective woman leader or scientist)41;
  • perspective taking (e.g., imagine in detail what it is like to be a person in a stereotyped group)42;
  • individuation (e.g., gather specific information about a student, patient, or applicant to prevent group stereotypes from leading to potentially inaccurate assumptions43; and
  • increasing opportunities for contact with counterstereotypic exemplars (e.g., meet with senior women faculty to discuss their ideas and vision).34

We also presented two counterproductive strategies: stereotype suppression (i.e., attempting to be “gender blind”)44 and a strong belief in one’s ability to make objective judgments.45 Both of these have been shown to enhance the influence of stereotype-based bias on judgment. To facilitate behavioral change, participants immediately applied content through paired discussions, audience response, case studies conducted as readers’ theater, and a written commitment to action.31,46–48 As reminders to practice bias-reducing behaviors, participants received a folder containing workshop materials, a bibliography, and a bookmark listing the six forms of bias discussed and the five bias-habit-changing strategies.

Outcomes and follow-up

We evaluated the impact of the workshop with 13 primary outcome measures (Table 1):

Table 1:
Category and Description of Outcome Variables Compared Between 46 Departments Randomly Allocated to Receive a Gender-Bias-Habit-Reducing Workshop and 46 Control Departments, University of Wisconsin–Madison, September 2010–March 2012

Implicit gender bias.

To measure implicit bias, we used a version of the Implicit Association Test (IAT) that assessed the strength of associating male and female names with leader or supporter words.49

Awareness of gender bias.

We developed a nine-item survey based on existing research on gender equity and discussions with women faculty.50 Exploratory factor analysis led us to average responses to questions that assessed perceived benefit to society (societal benefit, three questions) and awareness of gender bias in one’s discipline (disciplinary bias, two questions), and retain single-item questions for personal concern about one’s performance on the IAT (IAT concern), perceived vulnerability to unintentional gender bias (bias vulnerability), awareness of one’s own subtle gender bias (personal bias awareness), and awareness of gender bias in one’s environment (environmental bias awareness).

Motivation to promote gender equity.

Plant and Devine’s51 research distinguishes between motivation that stems from internal sources (i.e., beliefs, values) and motivation that stems from external sources (i.e., the desire to appear unbiased to others). We assessed each of these dimensions with a single question (internal motivation and external motivation).

Self-efficacy, outcome expectations, and action.

We derived question content for these measures from existing research on gender equity, academic leadership, and social cognitive theory7,16,36,52,53; and themes that emerged from two focus groups with faculty and senior staff (four men and four women in total). We conceived of willingness to promote gender equity as dependent on individuals’ beliefs in their ability to do so (i.e., their self-efficacy for enacting gender equity) and their perceptions of the cost–benefit ratio of doing so (i.e., their outcome expectations).16 We developed questions to assess gender equity self-efficacy (five questions) and expectations of both benefits (gender equity positive outcome, five questions) and risks (gender equity negative outcome, five questions) of acting to promote gender equity. The risk responses were reverse scored. We also developed five questions that asked faculty whether they performed specific gender-equity-promoting actions on a regular basis (action). The responses to questions assessing each measure were averaged.

Two days before (baseline), three days after, and again three months after a scheduled workshop, faculty in experimental and control department pairs received an e-mail invitation to take an online survey assessing the 13 primary outcome measures. For the IAT we used response times to compute D-scores where higher numbers indicate a stronger association of male names with leader roles and female names with supporter roles than the reverse.49,54 The other measures used seven-point scales with higher numbers associated with greater movement toward behavioral change. Outcomes were collected and analyzed at the individual level. We hypothesized that post intervention, faculty in experimental versus control departments would demonstrate less association of male with leader and female with supporter on the IAT, show greater changes in proximal requisites of bias habit reduction (awareness, internal and external motivation, self-efficacy, and outcome expectations), and report engaging in more action to achieve academic gender equity.

To evaluate the impact of the intervention on department climate, we analyzed five questions from the Study of Faculty Worklife.55 This instrument measures faculty’s perception of and satisfaction with the climate within their departments or units, as well as the overall institutional climate.55 This survey was mailed to all faculty prior to and after the workshop was developed and implemented. In the 92 study departments, the survey was sent to 2,495 faculty in 2010, and 2,460 in 2012. (The participant numbers vary from the 2,290 in our study because they were retrieved from a different administrative database.) Questions used five-point Likert scales and queried the extent to which, within their own departments, respondents felt isolated, felt they “fit in,” felt their colleagues solicited their opinions on work-related matters, felt their colleagues valued their research and scholarship, and felt comfortable raising personal/family responsibilities when scheduling department obligations. We hypothesized that if the intervention led faculty to break the gender bias habit, this would translate into broader changes in department culture leading faculty in experimental departments to respond more positively to questions about department climate.

Statistical analysis

Analysis was on an intention-to-treat basis with all randomized departments. We used linear mixed-effects models with 95% confidence intervals to compare responses from faculty in experimental versus control departments to the 13 primary outcome measures at each time point.56,57 The mean difference between experimental and control departments at follow-up times, minus the baseline difference, indicated the treatment effect. All models were adjusted for gender and rank. Models included three random effects to address clustering: person IDs accounted for the repeated measures, department pair IDs accounted for variability between matched pairs, and the interaction between department allocation and department pair IDs accounted for treatment effect variability between matched pairs.58–61 We used likelihood ratio tests to obtain P values62 and considered P values less than .05 to indicate statistical significance. All analyses were performed using R’s lme4 package with residual maximum likelihood estimation.62

Dose–response analysis for action.

We segregated action results from experimental department faculty and modeled the impact of the percentage of faculty (in quartiles) that attended the workshop [(0% to < 14%) versus (14% to < 25%) versus (25% to < 42%) versus (42% to < 91%)]. This model designated person IDs and department IDs as random effects.

Analysis of department climate questions.

We used baseline-adjusted linear mixed-effects models to analyze the department climate questions.57 Postintervention scores were corrected for prescores and analyzed on the basis of department allocation, gender, rank, and the two-way interaction effect between department allocation and gender. Models designated department pair IDs and the interaction between department allocation and department pair IDs as random effects.58–60


Figure 2 presents the CONSORT flow diagram.63 Of the 46 experimental departments, 43 received the intervention because none of the invited faculty in three departments attended their scheduled workshop. Data from these three departments were retained in the analyses. No department was lost to follow-up. Workshop attendance varied from 0 (in 3 departments with 8, 17, and 15 members) to 90% (19 of 21 members) of a department’s faculty (mean = 31%, SD = 21). Of the 1,137 faculty invited to a workshop, 301 attended (26%). The chair (or division head) from 72% of departments (33/46) attended their scheduled workshop. Overall, 52% of faculty in experimental departments (587/1,137 [578/1,097 from 43 departments whose faculty attended plus 9/40 from the 3 departments with no attendance]) and 49% of faculty in control departments (567/1,153) responded to the online surveys at least once (Figure 2). Response rates within matching categories were similar across experimental and control departments (Table 2). Full professors were slightly overrepresented among respondents in experimental departments where 494 respondents reported rank (152 [31%] assistant, 122 [25%] associate, and 220 [45%] full professor) and control departments where 471 respondents reported rank (134 [28%] assistant, 120 [25%] associate, and 217 [46%] full professor) compared with all departments ([N = 2,290], 797 [35%] assistant, 546 [24%] associate, and 947 [41%] full professor). Women also had higher representation among respondents in the experimental departments (204/603; 34%) and control departments (180/571; 32%) than all departments (695/2,290; 30%).

Table 2:
Description of Faculty in 46 Control Departments and 46 Experimental Departments Who Responded at Least Once to a Survey Sent Before, Three Days After, and Three Months After Experimental Departments Received a Gender-Bias-Habit-Reducing Workshop, University of Wisconsin–Madison, September 2010–March 2012
Figure 2:
Flow diagram of faculty gender-bias-reduction intervention depicting enrollment, department allocation and intervention, and survey response rates, from a study of 46 experimental and 46 control departments, University of Wisconsin–Madison, 2010–2012. Information presented is in accordance with flow diagram requirements of the Consolidated Standards of Reporting Trials (CONSORT) statement extended to cluster randomized trials.

Baseline responses from faculty in experimental versus control departments were not significantly different on any of the 13 primary outcome measures. At three days post intervention, faculty in experimental departments showed significantly greater increases in personal bias awareness (P = .009), internal motivation (P = .028), gender equity self-efficacy (P = .026), and gender equity positive outcome (P = .039) (Table 3). At three months post intervention, differences persisted in personal bias awareness (P = .001) and gender equity self-efficacy (P = .013), with experimental department faculty also showing an increase in external motivation (P = .026). Baseline IAT scores showed 66% (377/569) of faculty (68% male [224/330] and 64% female respondents [153/239]) with a slight, moderate, or strong automatic association of male with leader and female with supporter; 21% (122/569) showed no preference, and 12% (70/569) showed stronger female leader bias. IAT scores did not change significantly. There were no differences in action. However, when at least 25% of a department’s faculty attended the workshop (26 of 46 experimental departments), we found a significant increase in action at three months [(25% to < 42%) attendance, P = .007; (42% to < 91%) attendance, P = .006]. Department chair/head attendance had no effect on any outcome measure.

Table 3:
Mean Difference for Outcome Variables at Three Days and Three Months, Minus the Baseline Difference, and 95% Confidence Intervals (CIs) for 46 Experimental and 46 Control Departments Following a Gender-Bias-Habit-Reducing Intervention, University of Wisconsin–Madison, September 2010–March 2012a

Similar percentages of faculty in the experimental and control departments responded to the Study of Faculty Worklife survey, in both 2010 and 2012. In 2010, 48% of faculty responded overall (545/1,144 from experimental departments and 652/1,351 from control departments), and in 2012, 43% of faculty responded (470/1,145 from experimental departments and 590/1,315 from control departments). Some subgroups of faculty tended to respond more frequently to the survey in both waves. Women respond at higher rates than men; 62% (552/893) of women responded in either 2010 or 2012, and 53% (1,024/1,921) of men responded to at least one wave. Full professors responded more than assistant or associate professors; 61% (691/1,126) of full professors responded in at least one wave, whereas 52% (885/1,688) of faculty at lower ranks responded in either 2010 or 2012.

There were no significant baseline differences between experimental and control faculty’s responses to any department climate question. Model estimates of the treatment effect showed reduced standard errors after baseline adjustment.64 Post intervention, faculty in experimental departments felt that they “fit in” better (P = .024), that their colleagues valued their research and scholarship more (P = .019), and that they were more comfortable raising personal and family responsibilities in scheduling department obligations (P = .025) (Table 4). Results were consistent across male and female faculty, and workshop attendance by the department chair/head had no impact.

Table 4:
Baseline Adjusted Differences in Responses to Questions About Department Climate From 671 Faculty: 375 Respondents From 46 Departments That Received a Gender-Bias-Habit-Reducing Intervention and 296 Respondents From 46 Control Departments, University of Wisconsin–Madison, 2010–2012


To our knowledge, this is the first randomized, controlled study of a theoretically informed intervention to change faculty behavior and improve department climate in ways that would be predicted to promote gender equity and foster women’s academic career advancement. Faculty in departments exposed to the gender-bias-habit-reducing intervention demonstrated immediate boosts in several proximal requisites of intentional behavioral change: personal awareness, internal motivation, perception of benefits, and self-efficacy to engage in gender-equity-promoting behaviors. The sustained increase in self-efficacy beliefs at three months provides strong evidence of the effectiveness of the intervention.52 Self-efficacy is the cornerstone of widely accepted behavioral change theories.16,18,52 Positive outcome expectations are also important in promoting behavioral change16 and increased at three days after the intervention. When at least 25% of a department’s faculty attended the workshop, self-reports of actions to promote gender equity increased significantly at three months. This finding is consistent with research on critical mass,65 the importance of psychological safety in organizational change,66 and the collective dynamics of behavioral change in social networks.67 The increase in external motivation at three months supports a change in departmental norms for gender-equity-promoting behaviors.

A number of educational offerings have focused on increasing awareness of stereotype-based bias, particularly when it operates unintentionally and unwittingly.68,69 Our findings suggest that only to the extent that these initiatives can increase awareness of personal bias might they help promote behavioral change. In contrast to other educational offerings on stereotype-based bias,68,69 our workshop promoted self-efficacy beliefs by providing participants with specific behaviors to deliberately practice.70 They were asked to envision and write down specifically how they would enact these behaviors in the context of their own personal and professional lives. Similar written commitments to action have been found to promote behavioral change in other settings.47

We are the first to administer the gender/leadership IAT to faculty, and our findings indicated that the majority of male and female faculty have at least a slight bias linking male with leadership and female with supporter. Although faculty may be unaware of this implicit bias, a large body of research predicts that such bias will subtly advantage men and disadvantage women being evaluated as leaders or for leadership opportunities.36,40,71,72 Although the IAT score decreased for faculty in experimental departments, there was no significant difference compared with control departments. Our findings suggest that gender bias habit reduction can occur and department climate can be improved even in the face of persistent implicit gender/leadership bias as measured with an IAT.

Institutional transformation requires changes at multiple levels, yet it is the individuals within an institution who drive change.19,20,73 Consistent with these tenets of institutional change, we found that an intervention that helped individual faculty members change their gender bias habits led to positive changes in perception of department climate: increased perceptions of fit, valuing of research, and comfort in raising personal and professional conflicts. As with Fried and colleagues’1 structural interventions to promote gender equity, our educational/behavioral intervention appeared to benefit men and women because both male and female faculty in experimental departments perceived improved climate.

This study is limited to a single institution; however, it included faculty from a wide spectrum of medicine, science, and engineering disciplines. At 31%, the average workshop attendance by a department’s faculty was relatively low; however, this attendance rate is realistic for an activity that involves busy faculty, and it bodes well for the ability of our intervention to translate into practice at other academic institutions. Multiple outcome measures, as in our study, increase the likelihood of a statistically significant finding by chance alone. However, it is unlikely that a test would be significant at both postintervention time points by chance as with self-efficacy, that all significant differences would be in the predicted direction, and that experimental departments would also show improvements in climate relative to control departments on a separate survey. Because this is the first study to investigate the impact of an intervention to reduce habitual gender bias among faculty, there are no existing data against which to benchmark the “clinical” significance of the observed changes in participants’ pre- to posttreatment scores. The effect size as defined for educational research74 for the statistically significant results found in our study ranged from 0.11 to 0.32. It is not unusual in psychological, educational, and behavioral research for interventions with such relatively low effect sizes to be considered meaningful.75 As in our study, the outcomes for a number of such studies are measured by self-report. Our small effect size appears to be associated with meaningful impact because our intervention improved department climate including perceptions of work–life flexibility, which have been linked to retaining and promoting women in academic medicine.3,4,76 Another limitation is the possibility of response bias. Full professors and women were slightly overrepresented relative to all departments among respondents. This overrepresentation occurred in both experimental and control departments in the main study and in the work–life survey, reducing the likelihood that response bias accounted for the differences we observed. Overall, approximately half of the participants in both experimental and control departments responded at least once to the online surveys. Among these groups, our analytic method helps mitigate response bias because it includes all respondents regardless of their missing data patterns. We also ran two sensitivity analyses: one with the proportion of respondents at each time point, and another based on the difference between time points with only respondents that had data at both time points. Both analyses showed little change in the estimated effects, indicating that response bias had little influence on our comparisons between respondents in experimental and control groups.

In summary, this study makes the following new contributions to the field of gender equity in academic medicine, science, and engineering: The majority of male and female faculty had gender-stereotype-congruent leadership bias (male leader/female supporter), an intervention that approached gender bias as a remediable habit was successful in promoting gender equity behaviors among faculty, and the change in faculty behaviors seemed to improve the department climate for male and female faculty in medicine, science, and engineering departments.

Acknowledgments: The authors thank Tara Becker and Marjorie Rosenberg for their statistical contributions in launching this study and in early analyses of the data.


1. Fried LP, Francomano CA, MacDonald SM, et al. Career development for women in academic medicine: Multiple interventions in a department of medicine. JAMA. 1996;276:898–905
2. Pololi LH, Civian JT, Brennan RT, Dottolo AL, Krupat E. Experiencing the culture of academic medicine: Gender matters, a national study. J Gen Intern Med. 2013;28:201–207
3. Valantine H, Sandborg CI. Changing the culture of academic medicine to eliminate the gender leadership gap: 50/50 by 2020. Acad Med. 2013;88:1411–1413
4. Westring AF, Speck RM, Sammel MD, et al. A culture conducive to women’s academic success: Development of a measure. Acad Med. 2012;87:1622–1631
5. Magrane D, Helitzer D, Morahan P, et al. Systems of career influences: A conceptual model for evaluating the professional development of women in academic medicine. J Womens Health (Larchmt). 2012;21:1244–1251
6. National Academy of Sciences, National Academy of Engineering, Institute of Medicine of the National Academies. Beyond Biases and Barriers: Fulfilling the Potential of Women in Academic Science and Engineering. 2006 Washington, DC National Academies Press
7. Hill C, Corbett C, St. Rose A Why So Few? Women in Science, Technology, Engineering, and Mathematics. 2010 Washington, DC American Association of University Women
8. Corrice A. Unconscious bias in faculty and leadership recruitment: A literature review. AAMC Analysis in Brief. 2009;9:1–2 August
9. Moss-Racusin CA, Dovidio JF, Brescoll VL, Graham MJ, Handelsman J. Science faculty’s subtle gender biases favor male students. Proc Natl Acad Sci U S A. 2012;109:16474–16479
10. Isaac C, Lee B, Carnes M. Interventions that affect gender bias in hiring: A systematic review. Acad Med. 2009;84:1440–1446
11. Biernat MNelson T. Stereotypes and shifting standards. Handbook of Stereotyping and Prejudice. 2009 New York, NY Psychology Press:137–152
12. Devine PG. Stereotypes and prejudice: Their automatic and controlled components. J Pers Soc Psychol. 1989;56:5–18
13. Devine PG, Forscher PS, Austin AJ, Cox WTL. Long-term reduction in implicit race prejudice: A prejudice habit-breaking intervention. J Exp Soc Psychol. 2012;48:1267–1278
14. Carnes M, Devine PG, Isaac C, et al. Promoting institutional change through bias literacy. J Divers High Educ. 2012;5:63–77
15. Carnes M, Handelsman J, Sheridan J. Diversity in academic medicine: The stages of change model. J Womens Health (Larchmt). 2005;14:471–475
16. Bandura A. Social cognitive theory of self-regulation. Organ Behav Hum Decision Proc. 1991;50:248–287
17. Prochaska JO, Velicer WF, Rossi JS, et al. Stages of change and decisional balance for 12 problem behaviors. Health Psychol. 1994;13:39–46
18. Prochaska JO, DiClemente CC, Norcross JC. In search of how people change: Applications to addictive behaviors. Am Psychol. 1992;47:1102–1114
19. Nonaka I.. A dynamic theory of organizational knowledge creation. Org Sci. 1994;5:14–37
20. Greenhalgh T, Robert G, Macfarlane F, Bate P, Kyriakidou O. Diffusion of innovations in service organizations: Systematic review and recommendations. Milbank Q. 2004;82:581–629
21. Campbell C, O’Meara K. Faculty agency: Departmental contexts that matter in faculty careers. Res High Educ. 2013;54 Accessed September 12, 2014
22. Carr PL, Szalacha L, Barnett R, Caswell C, Inui T. A “ton of feathers”: Gender discrimination in academic medical careers and how to manage it. J Womens Health (Larchmt). 2003;12:1009–1018
23. Settles IH, Cortina LM, Malley J, Stewart AJ. The climate for women in academic science: The good, the bad, and the changeable. Psychol Women Q. 2006;30:47–58
24. Donner A, Klar N Design and Analysis of Cluster Randomization Trials in Health Research. 2002 London, UK Arnold
25. Schulz KF, Grimes DA. Blinding in randomised trials: Hiding who got what. Lancet. 2002;359:696–700
26. Schulz KF, Grimes DA. Allocation concealment in randomised trials: Defending against deciphering. Lancet. 2002;359:614–618
27. Imai K, King G, Nall C. The essential role of pair matching in cluster-randomized experiments, with application to the Mexican Universal Health Insurance evaluation. Stat Sci. 2009;24:29–53
28. Ivers NM, Halperin IJ, Barnsley J, et al. Allocation techniques for balance at baseline in cluster randomized trials: A methodological review. Trials. 2012;13:120
29. Boutron I, Moher D, Altman DG, Schulz KF, Ravaud PCONSORT Group. . Extending the CONSORT statement to randomized trials of nonpharmacologic treatment: Explanation and elaboration. Ann Intern Med. 2008;148:295–309
30. Murray DM Design and Analysis of Group-Randomized Trials. 1998;Vol 29 New York, NY Oxford University Press
31. Knox A Helping Adults Learn. 1986 San Francisco, Calif Jossey-Bass
32. Knowles MS The Adult Learner: A Neglected Species. 19902nd ed. Houston, Tex Gulf Publishing
33. Sevo R, Chubin DE Bias Literacy: A Review of Concepts in Research on Discrimination. 2008 Washington, DC American Association for the Advancement of Science Center for Advancing Science & Engineering Capacity
34. Allport GW The Nature of Prejudice. 1979 Reading, Mass Addison-Wesley Publishing Company
35. Burgess D, Borgida E. Who women are, who women should be: Descriptive and prescriptive gender stereotyping in sex discrimination. Psychol Public Policy Law. 1999;5:665–692
36. Eagly AH, Karau SJ. Role congruity theory of prejudice toward female leaders. Psychol Rev. 2002;109:573–598
37. Uhlmann E, Cohen GL. Constructed criteria: Redefining merit to justify discrimination. Psychol Sci. 2005;16:474–480
38. Banaji MR, Hardin C, Rothman AJ. Implicit stereotyping in person judgment. J Pers Soc Psychol. 1993;65:272–281
39. Carnes M, Geller S, Fine E, Sheridan J, Handelsman J. NIH Director’s Pioneer Awards: Could the selection process be biased against women? J Womens Health (Larchmt). 2005;14:684–691
40. Burgess DJ, Joseph A, van Ryn M, Carnes M. Does stereotype threat affect women in academic medicine? Acad Med. 2012;87:506–512
41. Blair IV, Ma JE, Lenton AP. Imagining stereotypes away: The moderation of implicit stereotypes through mental imagery. J Pers Soc Psychol. 2001;81:828–841
42. Galinsky AD, Moskowitz GB. Perspective-taking: Decreasing stereotype expression, stereotype accessibility, and in-group favoritism. J Pers Soc Psychol. 2000;78:708–724
43. Heilman ME. Information as a deterrent against sex discrimination: The effects of applicant sex and information type on preliminary employment decisions. Organ Behav Hum Perform. 1984;33:174–186
44. Monteith MJ, Sherman JW, Devine PG. Suppression as a stereotype control strategy. Pers Soc Psychol Rev. 1998;2:63–82
45. Uhlmann EL, Cohen GL. “I think it, therefore it’s true”: Effects of self-perceived objectivity on hiring discrimination. Organ Behav Hum Decision Proc. 2007;104:207–223
46. Herreid CF. Case study teaching. New Dir Teach Learn. Winter 2011;128:31–39
47. Overton GK, MacVicar R. Requesting a commitment to change: Conditions that produce behavioral or attitudinal commitment. J Contin Educ Health Prof. 2008;28:60–66
48. Sheridan JT, Fine E, Pribbenow CM, Handelsman J, Carnes M. Searching for excellence and diversity: Increasing the hiring of women faculty at one academic medical center. Acad Med. 2010;85:999–1007
49. Dasgupta N, Asgari S. Seeing is believing: Exposure to counterstereotypic women leaders and its effect on the malleability of automatic gender stereotyping. J Exp Soc Psychol. 2004;40:642–658
50. Sheridan J, Brennan PF, Carnes M, Handelsman J. Discovering directions for change in higher education through the experiences of senior women faculty. J Technol Transf. 2006;31:387–396
51. Plant EA, Devine PG. Internal and external motivation to respond without prejudice. J Pers Soc Psychol. 1998;75:811–832
52. Bandura A Self-Efficacy: The Exercise of Control. 1997 New York, NY Worth Publishers
53. Isaac C, Kaatz A, Lee B, Carnes M. An educational intervention designed to increase women’s leadership self-efficacy. CBE Life Sci Educ. 2012;11:307–322
54. Greenwald AG, Nosek BA, Banaji MR. Understanding and using the implicit association test: I. An improved scoring algorithm. J Pers Soc Psychol. 2003;85:197–216
55. Sheridan JT. Study of Faculty Worklife at the University of Wisconsin–Madison. Accessed September 12, 2014
56. West BT, Welch KB, Galecki AT Linear Mixed Models: A Practical Guide Using Statistical Software. 2007 Boca Raton, Fla Chapman & Hall/CRC
57. Hox JJBalderjahn I, Mathar R, Schader M. Multilevel modeling: When and why. In: Classification, Data Analysis and Data Highways. 1998 New York, NY Springer:147–154
58. Klar N, Donner A. The merits of matching in community intervention trials: A cautionary tale. Stat Med. 1997;16:1753–1764
59. Thompson SG. The merits of matching in community intervention trials: A cautionary tale. Stat Med. 1998;17:2149–2152
60. Thompson SG, Pyke SD, Hardy RJ. The design and analysis of paired cluster randomized trials: An application of meta-analysis techniques. Stat Med. 1997;16:2063–2079
61. Ivers NM, Taljaard M, Dixon S, et al. Impact of CONSORT extension for cluster randomised trials on quality of reporting and study methodology: Review of random sample of 300 trials, 2000–8. BMJ. 2011;343:d5886
62. Bates DM. lme4: Mixed-Effects Modeling with R Accessed September 12, 2014
63. Campbell MK, Elbourne DR, Altman DGCONSORT group. . CONSORT statement: Extension to cluster randomised trials. BMJ. 2004;328:702–708
64. Garofolo KM, Yeatts SD, Ramakrishnan V, Jauch EC, Johnston KC, Durkalski VL. The effect of covariate adjustment for baseline severity in acute stroke clinical trials with responder analysis outcomes. Trials. 2013;14:98
65. Centola DM. Homophily, networks, and critical mass: Solving the start-up problem in large group collective action. Rationality Soc. 2013;25:3–40
66. Edmondson AC, Kramer RM, Cook KS. Psychological safety, trust, and learning in organizations: A group-level lens. Trust and Distrust in Organizations: Dilemmas and Approaches. 2004 New York, NY Russell Sage Foundation:239–272
67. Christakis NA, Fowler JH. The collective dynamics of smoking in a large social network. N Engl J Med. 2008;358:2249–2258
68. Assocation of American Medical Colleges. . E-Learning Seminar: What You Don’t Know: The Science of Unconscious Bias and What To Do About It in the Search and Recruitment Process. Accessed September 12, 2014
69. National Center for State Courts. . Helping Courts Address Implicit Bias: Strategies to Reduce the Influence of Implicit Bias. Accessed September 12, 2014
70. Ericsson KA, Karmpe RT, Tesch-Romer C. The role of deliberate practice in the acquisition of expert performance. Psychol Rev. 1993;100:363–406
71. Ridgeway CL. Gender, status, and leadership. J Soc Issues. 2001;57:637–655
72. Heilman M. Description and prescription: How gender stereotypes prevent women’s ascent up the organizational ladder. J Soc Issues. 2001;57:657–674
73. Rogers EM Diffusion of Innovations. 19955th ed. New York, NY Free Press
74. Glass GV. Primary, secondary, and meta-analysis of research. Educ Res. 1976;5:3–8
75. Lipsey MW, Wilson DB. The efficacy of psychological, educational, and behavioral treatment. Confirmation from meta-analysis. Am Psychol. 1993;48:1181–1209
76. Foster SW, McMurray JE, Linzer M, Leavitt JW, Rosenberg M, Carnes M. Results of a gender-climate and work-environment survey at a midwestern academic health center. Acad Med. 2000;75:653–660
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