The value of traditional interviews in predicting medical school applicants’ academic and clinical performance has been questioned, partly because of concerns about reliability.1–4 The Multiple Mini-Interview (MMI), in which applicants work through a series of brief, semistructured assessment stations attended by trained raters, was developed in response to these concerns3 and has been adopted by many medical schools in Canada, the United States, and Australia.5–7 Preliminary research suggests that the MMI is well accepted by applicants,8 is reasonably reliable, and may predict aspects of performance in medical school as well as on preresidency licensure examinations.5–7
It is unclear, however, whether certain applicant characteristics predict MMI performance and whether MMI performance influences medical schools’ subsequent decisions to extend acceptance offers. Applicant personality may affect both, given that the MMI requires students to engage in a rapid sequence of challenging, moderately stressful situations. Among personality models, research supports the use of the five-factor model (FFM) of agreeableness, conscientiousness, extraversion, neuroticism, and openness in predicting myriad human abilities.9–16 Specific abilities related to individuals’ personality factor scores include critical thinking, ethical decision making, and interpersonal communication,17–22 all of which are purportedly assessed by the MMI.3
If applicant personality affects MMI performance and also influences decisions to extend medical school acceptance offers, incorporating the MMI into the admissions process may have salutary and detrimental consequences. For example, the MMI may help identify applicants with high levels of conscientiousness, the personality factor that is the most strongly predictive of superior medical school academic performance and clinical performance.18,23–27 However, it may also select for personality factors that are not as consistently linked with superior medical student and physician performance as is conscientiousness. Further, if the MMI were to select for specific personalities, its use could result in the admission of relatively homogeneous medical school classes, which could reduce the diversity of student attitudes, thoughts, and behaviors and decrease opportunities for students to experience personal and professional growth. In addition, personality has been associated with physician specialty choice,28,29 so there could be important physician training and workforce implications.
Studies in Canada30 and Australia31 have reported mixed findings concerning the relationship of the FFM with MMI performance. As medical school selection processes differ among countries, we analyzed admissions data from one U.S. medical school to explore the associations of the five personality factors with MMI performance, as well as the associations of personality factors and MMI performance with medical school acceptance offers.
Method
We conducted this observational study during the September 2010 to July 2011 admissions cycle at the University of California, Davis (UCD) School of Medicine (SOM). We invited all applicants who attended an MMI session during this cycle to participate by completing a questionnaire designed to measure personality factors. The UCD institutional review board approved the study protocol.
Applicant screening
All applicants applied via the American Medical College Application Service (AMCAS). On the basis of application review using criteria that included Medical College Admission Test (MCAT) scores, admissions personnel invited screened applicants to submit a secondary application. Faculty evaluated the completed secondary applications on the basis of MCAT scores, personal statements, extracurricular experiences, and recommendation letters, to select applicants to invite to an MMI session. Each invited applicant self-scheduled his or her MMI session date online; sessions were held from September 2010 through March 2011.
The MMI: Process and scoring
The MMI consisted of 10 individual timed stations, each lasting 10 minutes. Applicants completed 10 stations consecutively; the starting station and station completion order varied among applicants in nonrandom fashion. Before entering the room at each station, applicants had two minutes to read a brief summary of the station’s focus. After entering the room, applicants had eight minutes to address the assigned tasks. Nine stations were designed to facilitate assessment of cognitive and/or noncognitive skills in one or more of the following areas: integrity/ethics, professionalism, interpersonal communication, diversity/cultural awareness, teamwork, ability to handle stress, and problem solving. The 10th station asked applicants to explain their choice to pursue a career in medicine. Content for most stations was adapted from ProFitHR (Advanced Psychometrics for Transitions Inc., Hamilton, Ontario, Canada; http://www.profithr.com/).
Each station was observed by a single trained rater, who had no information regarding applicants’ academic or personal backgrounds. Raters from a variety of backgrounds participated, including full-time and volunteer faculty, resident physicians, medical students, nurses, administrative staff, pharmacists, and community members. At some stations, raters interacted with applicants, whereas at others they only observed applicant behavior (e.g., interactions with an actor or another applicant).
At each of the 10 stations, raters scored applicant performance using a four-point scale (range: 0–3 points, with higher scores indicating better performance). For this study, we summed individual station scores to yield a total MMI score for each respondent (range: 0–30). Cronbach alpha for the score in this study was 0.68 (comparable to that observed in other studies),3,6 and the average interstation score correlation was 0.18.
Personality factors: Questionnaire and scoring
At the end of each MMI session, all applicants who participated attended a group debriefing meeting led by the associate dean of admissions (M.H.). Immediately following the debriefing, and after the associate dean exited the room, a research assistant asked the applicants to complete a paper questionnaire designed to measure their FFM personality factors. Applicants were informed verbally and in writing that questionnaire completion was voluntary and that their participation decision and (if they participated) their responses would not influence acceptance offers, as selection personnel could not access their responses. They were asked to record their AMCAS identification numbers on the form (but no other identifying information) to allow linkage of their responses with other study variables.
The personality questionnaire we employed was the Big Five Inventory (BFI), a validated measure typically completed in less than five minutes.32–34 The BFI consists of 44 statements assessing agreeableness (9 items), conscientiousness (9 items), extraversion (8 items), neuroticism (8 items), and openness (10 items). Table 1 summarizes the dispositional tendencies associated with high and low levels of these personality factors and provides example statements from the BFI scales measuring each factor.16,33 Respondents indicate their degree of disagreement or agreement with each statement using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). Sixteen items are reverse coded before scoring. For each respondent, we averaged the scores across all items related to each personality factor to yield a mean factor score (range: 1–5, with higher scores indicating higher levels). Cronbach alphas in this study were as follows: agreeableness, 0.80; conscientiousness, 0.82; extraversion, 0.88; neuroticism, 0.81; and openness, 0.75.
;)
Table 1: Dispositional Tendencies Within the Five-Factor Model Personality Traits and Example Big Five Inventory (BFI) Items Measuring Each
Other measures
We defined acceptance offers (versus not) as official offers to attend the medical school extended by the dean of admissions after considering all application materials, MMI performance, impressions from small-group meetings with applicants, and selection subcommittee recommendations.
In addition to MMI scores, the admissions office provided us with information extracted from AMCAS applications regarding the following characteristics: age; sex; self-identified race, ethnicity, and disadvantaged status; state of residence; cumulative grade point average (GPA); and MCAT scores. The admissions office provided these data to the research assistant in an electronic spreadsheet that identified applicants only by their AMCAS numbers. The research assistant added BFI data and, to maintain anonymity, replaced AMCAS numbers with unique study identification numbers before sending the data file to the study investigators for analysis.
Data analysis
We analyzed the data using Stata version 11.2 (Stata Corporation, College Station, Texas). We employed the chi-square test (for categorical variables) and t test (for continuous variables) to compare characteristics of respondents who were offered acceptance with those who were not.
We employed two linear regression models to examine associations between respondent characteristics and total MMI score (the dependent variable). The first model included the following covariates: age (19–21, 22, 23–24, or 25–39 [reference]); female sex (yes/no); any self-identified Hispanic ethnicity (solely Hispanic or multiethnic including Hispanic); any self-identified white race (solely white or multiracial including white); self-identified disadvantaged status (yes/no); California resident (yes/no); cumulative GPA and MCAT Verbal Reasoning, Physical Sciences, and Biological Sciences scores (continuous measures); and, to capture potential changes in the tendency to offer acceptance during the admissions screening cycle, date of MMI participation by quartile (September 3 to October 1, 2010 [reference], October 8 to December 3, 2010, December 10, 2010 to January 28, 2011, or February 4 to March 18, 2011). The second linear regression model included all of the covariates in the first model, plus scores on each of the five personality factors (by quartile, with the lowest quartile scores as reference). To examine the additional contribution of the personality factors to the MMI score, we determined the adjusted model R2 with and without the personality factors.
We employed three sequential logistic regressions to model medical school acceptance (acceptance offer versus not), the dependent variable. The first model included the same covariates as the first linear regression, the second model added score on each of the five personality factors (by quartile, with the lowest quartile as reference), and the third model added the MMI score. To examine the additional contribution of the personality factors to an offer of acceptance, we calculated the model area under the receiver operating curve (ROC), or discrimination, for the first and second logistic regression models.
In all analyses, we defined statistical significance as a P value < .05.
Results
Of the 4,792 individuals who submitted an AMCAS application to the UCD SOM in the 2010–2011 admissions cycle, 2,796 (58.3%) were invited to submit a secondary application. Of the 2,482 (88.8%) submitting a secondary application, 517 (20.8 %) were invited to attend an MMI session. Of the 458 (88.6%) participating in an MMI session, 444 (96.9%) completed the BFI and had complete MMI scoring data. These 444 respondents constituted the analytic sample for this study.
Table 2 summarizes the characteristics of the sample by acceptance offer status. Compared with respondents who were not offered acceptance, those who were offered acceptance were statistically significantly more likely to be female and to have higher MMI scores, lower MCAT Physical Sciences scores, and higher extraversion and agreeableness scores (P ≤ .01 for all of these characteristics). Of the five personality factors, only extraversion was statistically significantly correlated with MMI score (r = 0.35, P < .01).
Table 2: Characteristics of 2010–2011 University of California, Davis, School of Medicine Multiple Mini-Interview (MMI) Participants Completing a Personality Questionnaire (N = 444), by Acceptance Offer Status*
Table 3 presents the results of the two linear regression models examining associations of respondents’ characteristics with MMI score (one model without and one with personality factors). Controlling for all other model covariates, applicants aged 19 to 21 had a statistically significantly lower mean total MMI score (P = .02) compared with applicants aged 25 to 39 (the reference age category). MMI score was also statistically significantly associated with female sex (P < .01) and MCAT Verbal Reasoning score (P = .03).
Table 3: Results of Linear Regression Models Examining Associations of Applicant Characteristics With Multiple Mini-Interview (MMI) Score Among 2010–2011 University of California, Davis, School of Medicine MMI Participants Completing a Personality Questionnaire (N = 444)*
Controlling for all other model covariates, respondents with extraversion in higher quartiles had statistically significantly higher total MMI scores (P < .01 for each of the three higher extraversion quartiles) compared with those in the lowest extraversion quartile. Higher quartile agreeableness, conscientiousness, neuroticism, and openness were not statistically significantly associated with MMI score compared with their respective lowest quartile reference categories. The model R2 was 5.6% when the personality variables were excluded and 15.2% when they were included.
Table 4 presents the results of the three sequential logistic regression models (base model without personality or MMI score, second model adding personality, third model adding MMI score) examining associations of respondent characteristics with medical school acceptance offers. In the second model, controlling for other model covariates, two personality factors were statistically significantly associated with acceptance offers: agreeableness (P = .01, .01, and <.01 for each of the three higher quartiles versus the lowest quartile [reference]) and extraversion (P = .01, .05, and <.01 for each of the three higher quartiles versus the lowest quartile [reference]). Compared with the reference age category (25–39), and controlling for other covariates, applicants aged 19 to 21 were statistically significantly less likely to be offered acceptance (P = .01). The ROC for the base prediction model, which excluded the personality variables, was 62.2%; it increased to 70.6% in the second model, which added the personality variables. In the third model, which added the MMI score, the statistically significantly positive association of agreeableness with acceptance offers was little changed (P = .04, .02, and <.01 for agreeableness in each of the three higher quartiles versus the lowest quartile [reference]), but extraversion and age were no longer statistically significantly associated with acceptance offers. Higher MMI scores (P < .01), California residence (P = .02), and higher MCAT Biological Sciences scores (P = .01) were also statistically significantly associated with acceptance offers.
Table 4: Results of Logistic Regression Models Examining Associations of Applicant Characteristics With Medical School Acceptance Offers Among 2010–2011 University of California, Davis, School of Medicine Multiple Mini-Interview (MMI) Participants Completing a Personality Questionnaire (N = 444)*
Discussion
Our results suggest that applicant personality influences aspects of the medical school admissions process, including performance on the MMI. Among the FFM personality traits, we found only higher extraversion to be independently associated with better MMI performance. Given the dispositional tendencies of individuals with higher extraversion (Table 1), this finding is consistent with the common characterization of the MMI as a “speed dating” exercise.35–37
Two prior studies examining associations between the FFM personality traits and MMI performance had mixed findings. Kulasegaram and colleagues30 found no significant associations in a sample of Canadian medical school applicants, but only 28% of that study’s MMI participants completed a personality measure. This low response rate potentially limits the generalizability of their findings, particularly because personality may influence questionnaire nonresponse.38 Griffin and Wilson’s31 findings were closer to ours: In their sample of Australian medical school applicants, higher extraversion, agreeableness, and conscientiousness were associated with higher MMI scores. However, the MMI participants in their study were younger than those in our study (they were just completing high school), and younger individuals typically have lower levels of agreeableness and conscientiousness than do older persons.39 Also, screening for MMI participation in the Australian study hinged solely on cognitive testing results. By contrast, screening of UCD SOM applicants to determine MMI participation included faculty ratings of personal statements, recommendation letters, and extracurricular experiences. Such ratings likely preselect candidates with higher agreeableness and conscientiousness (e.g., team orientation, humanistic qualities).11
Regarding the relationships of personality factors and MMI performance with acceptance offers, higher agreeableness (scores in the three higher quartiles), and higher extraversion (particularly scores in the highest quartile) were associated with acceptance offers in a model omitting MMI score. After adjusting for MMI score, MMI score was associated with acceptance offers; the association of agreeableness with acceptance offers changed little, but the extraversion association was no longer significant. Thus, higher agreeableness may be advantageous in aspects of the selection process other than the MMI, as noted above,40 whereas higher extraversion may influence both MMI ratings and acceptance offers.
Though these findings are exploratory and preliminary, they raise questions regarding the use of the MMI in the medical school admissions process. Although we found MMI performance to be associated with extraversion, the relationship of extraversion to medical student and physician performance is unclear. Findings vary regarding the association of extraversion with assessments of student knowledge.24,25,40,41 For example, poorer test performance among persons with higher extraversion may reflect a tendency to engage in extracurricular activities that detract from studying.24 In other studies, students with higher extraversion have tended to receive higher ratings of interpersonal behavior on clinical clerkship evaluations.25,40 Thus, unsurprisingly, being “socially ascendant, affectionate, and warm”25 may favorably influence ratings of interpersonal abilities, but the implications for physician competence are unclear. Physicians with higher extraversion might be able to quickly establish rapport with patients and persuade them to adhere to their recommendations, but they might also tend to dominate physician–patient interactions, thereby reducing patient participation and negatively affecting information gathering and diagnostic and therapeutic reasoning. The net effect of higher extraversion on physician behavior requires further study.
Our finding that extraversion was the only FFM trait associated with MMI performance suggests that the MMI process could contribute to reduced diversity of thoughts, attitudes, and behaviors in medical school classes, though this possibility requires further study. This finding, if confirmed by others, could be problematic for two reasons. First, an important aspect of medical training is exposure to viewpoints and ideas that challenge biases and beliefs, which facilitates personal and professional growth as well as learning to work collaboratively with a range of personalities. Second, there is a lack of evidence suggesting that high extraversion is critical for success in medicine. It seems more plausible that various combinations of personality factors would contribute to the development of an optimal physician workforce, given the range of career options and practice niches. Indeed, there is evidence that different personality factors may be associated with various medical specialty choices, though specific findings vary among studies.28,29,42 Although additional research is needed in this area, our findings suggest that wider adoption of the MMI could have unanticipated implications for graduate medical education and physician specialty distribution.
In our study, MMI score was not associated with conscientiousness, the personality factor most consistently found to independently predict performance in medical school and eventual physician practice. Conscientiousness predicts performance on preclinical and clinical evaluations, with evidence of increasing predictive validity as training proceeds.18,23–27 Conscientious individuals possess many of the attributes widely regarded as core attributes of effective physicians, such as attention to detail, follow-through on commitments, and internal motivation (Table 1).16 Better MMI performance may not have been associated with higher conscientiousness in this study because the UCD SOM screening process for MMI participation had already selected for conscientiousness. Different findings related to conscientiousness might be observed at medical schools employing different pre-MMI screening processes. It may also be possible to modify MMI elements to further select for conscientiousness, which is another issue for future study.
Future studies should also investigate whether traditional interviews, compared with MMIs, are more, less, or equally likely to favor applicants with particular personalities. The traditional interview process may be less susceptible than the MMI to influence by specific personality factors because of greater inter-interviewer variation in rating criteria and less process standardization. Although concerns exist that such characteristics of traditional interviews reduce their rating reliability,1–4 process modifications (e.g., use of structured rating forms, meetings among interviewers to reconcile discrepancies in ratings) may improve their reliability without sacrificing their potential advantages.43 We believe that such process modifications to traditional interviews merit further study, ideally in randomized trials, as an alternative to adoption of the MMI.
We also found that women, older applicants, and individuals with higher MCAT Verbal Reasoning scores had statistically significantly higher MMI scores compared with other applicants. MCAT Verbal Reasoning scores have been positively associated with interpersonal communication ratings in medical school,44 and women are more likely than men to communicate in ways that foster rapport building in new social situations.45,46 The implications of the latter finding for medical school admissions processes are unclear given that women, once under-represented in medicine, now constitute about half of U.S. medical school matriculants.47 Lastly, it makes sense that older applicants would have better MMI performance than younger applicants because they are more likely to have had prior life experiences requiring effective communication in high-stakes situations.
Limitations
This study had some limitations. It was observational, so causal associations cannot be inferred. The data were derived from MMI participants at a single medical school during one application cycle. Although the MMI process is fairly well standardized across schools, the content of stations and the rater-training process differ among schools. Thus, it is unclear whether our findings are generalizable to MMI participants at other schools; additional studies, ideally with multiple participating schools, would be helpful in confirming our findings. Although completing the study questionnaire was voluntary and applicants were informed that their BFI personality measure responses would not influence acceptance offers, given the high-stakes nature of MMI participation and medical school admissions, social desirability may have influenced responses to the BFI.
This study also has limitations associated with the use of BFI personality measures. First, BFI norms for medical school applicants have not been published. However, compared with the general population of similarly aged persons, our sample had higher mean agreeableness and conscientiousness scores, similar extraversion and openness scores, and lower neuroticism scores.39 Relatively high levels of agreeableness and conscientiousness might be expected in medical school applicants—particularly those selected to participate in the MMI using criteria that included reported extracurricular experiences—but our sample’s lower neuroticism score suggests some social desirability in responses. Studies in nonmedical fields have also found evidence of social desirability in responses to workplace personality questionnaires, though with minimal effects on predictive validity.48 In addition, the BFI is not focused on identifying individuals with disordered personalities, which is a matter of clear interest in medical school admissions. Future studies should examine associations of MMI performance and acceptance offers with applicants’ scores on measures designed to detect personality pathology.49,50
Conclusion
In conclusion, we found that of the FFM personality traits, only extraversion was associated with MMI performance. Furthermore, MMI performance, along with higher levels of agreeableness, was associated with medical school acceptance offers. Our findings suggest that applicant personality influences both MMI performance and the medical school admissions process. They also raise the concern that the widespread adoption of the MMI could result in a narrower range of student personalities in medical schools, possibly reducing diversity of thoughts, attitudes, and behaviors among medical students. These observations suggest that there is a need for multischool randomized trials to compare the reliability, predictive validity, and potential unintended consequences of the MMI process with those of traditional interviews.
Acknowledgments: The authors thank the following University of California, Davis, School of Medicine personnel: Miyishia Slay, Clinical and Translational Science Center, for data collection and entry; Ann Brunson, analyst, Office of the Dean, for admissions data retrieval and preparation; Nick Clark, Center for Healthcare Policy and Research, for additional data entry; Gurmeet Rai, manager, Office of Admissions, for facilitating access to MMI and other admissions data; and Kendra Harris, director of clinical skills education and assessment, for coordination of the MMI process.
Funding/Support: Funded in part by the Office of the Dean and by a research grant from the Department of Family and Community Medicine Research Grant, University of California, Davis, School of Medicine.
Other disclosures: None.
Ethical approval: Ethical approval for the study was granted by the University of California, Davis, institutional review board.
References
1. Albanese MA, Snow MH, Skochelak SE, Huggett KN, Farrell PM. Assessing personal qualities in medical school admissions. Acad Med. 2003;78:313–321
2. Edwards JC, Johnson EK, Molidor JB. The interview in the admission process. Acad Med. 1990;65:167–177
3. Eva KW, Rosenfeld J, Reiter HI, Norman GR. An admissions OSCE: The multiple mini-interview. Med Educ. 2004;38:314–326
4. Kreiter CD, Yin P, Solow C, Brennan RL. Investigating the reliability of the medical school admissions interview. Adv Health Sci Educ Theory Pract. 2004;9:147–159
5. Eva KW, Reiter HI, Rosenfeld J, Norman GR. The ability of the multiple mini-interview to predict preclerkship performance in medical school. Acad Med. 2004;79(10 suppl):S40–S42
6. Eva KW, Reiter HI, Trinh K, Wasi P, Rosenfeld J, Norman GR. Predictive validity of the multiple mini-interview for selecting medical trainees. Med Educ. 2009;43:767–775
7. Reiter HI, Eva KW, Rosenfeld J, Norman GR. Multiple mini-interviews predict clerkship and licensing examination performance. Med Educ. 2007;41:378–384
8. Razack S, Faremo S, Drolet F, Snell L, Wiseman J, Pickering J. Multiple mini-interviews versus traditional interviews: Stakeholder acceptability comparison. Med Educ. 2009;43:993–1000
9. Bogg T, Roberts BW. Conscientiousness and health-related behaviors: A meta-analysis of the leading behavioral contributors to mortality. Psychol Bull. 2004;130:887–919
10. Booth-Kewley S, Vickers RR Jr. Associations between major domains of personality and health behavior. J Pers. 1994;62:281–298
11. Costa PT, McCrae RR Revised NEO Personality Inventory and NEO Five Factor Inventory: Professional Manual.. 1992 Odessa, Fla Psychological Assessment
12. Roberts BW, Kuncel NR, Shiner R, Caspi A, Goldberg LR. The power of personality: The comparative validity of personality traits, socioeconomic status, and cognitive ability for predicting important life outcomes. Perspect Psychol Sci. 2007;2:313–345
13. Salgado JF. The Five Factor Model of personality and job performance in the European community. J Appl Psychol. 1997;82:30–43
14. Digman J. Personality structure: Emergence of the five factor model. Ann Rev Psychol. 1990;41:417–440
15. McCrae RR, Costa PT Jr. Personality trait structure as a human universal. Am Psychol. 1997;52:509–516
16. Goldberg LR. An alternative “description of personality”: The big-five factor structure. J Pers Soc Psychol. 1990;59:1216–1229
17. Ashton MC, Lee K. Honesty-humility, the big five, and the five-factor model. J Pers. 2005;73:1321–1353
18. Bore M, Munro D, Powis D. A comprehensive model for the selection of medical students. Med Teach. 2009;31:1066–1072
19. Chamberlain TC, Catano VM, Cunningham DP. Personality as a predictor of professional behavior in dental school: Comparisons with dental practitioners. J Dent Educ. 2005;69:1222–1237
20. Chapman BP, Duberstein PR, Epstein RM, Fiscella K, Kravitz RL. Patient-centered communication during primary care visits for depressive symptoms: What is the role of physician personality? Med Care. 2008;46:806–812
21. Clifford JS, Boufal MM, Kurtz JE. Personality traits and critical thinking skills in college students: Empirical tests of a two-factor theory. Assessment. 2004;11:169–176
22. Barrick MR, Mount MK. The Big Five personality dimensions and job performance: A meta-analysis. Pers Psychol. 1991;44:1–26
23. Enns MW, Cox BJ, Sareen J, Freeman P. Adaptive and maladaptive perfectionism in medical students: A longitudinal investigation. Med Educ. 2001;35:1034–1042
24. Lievens F, Coetsier P, De Fruyt F, De Maeseneer J. Medical students’ personality characteristics and academic performance: A five-factor model perspective. Med Educ. 2002;36:1050–1056
25. Lievens F, Ones DS, Dilchert S. Personality scale validities increase throughout medical school. J Appl Psychol. 2009;94:1514–1535
26. Ferguson E, Sanders A, O’Hehir FJ, James D. Predictive validity of personal statements and the role of the five-factor model of personality in relation to medical training. J Occup Organ Psychol. 2000;73:321–344
27. Doherty EM, Nugent E. Personality factors and medical training: A review of the literature. Med Educ. 2011;45:132–140
28. Hoffman BM, Coons MJ, Kuo PC. Personality differences between surgery residents, nonsurgery residents, and medical students. Surgery. 2010;148:187–193
29. Markert RJ, Rodenhauser P, El-Baghdadi MM, Juskaite K, Hillel AT, Maron BA. Personality as a prognostic factor for specialty choice: A prospective study of 4 medical school classes. Medscape J Med. 2008;10:49
30. Kulasegaram K, Reiter HI, Wiesner W, Hackett RD, Norman GR. Non-association between Neo-5 personality tests and multiple mini-interview. Adv Health Sci Educ Theory Pract. 2010;15:415–423
31. Griffin B, Wilson I. Associations between the big five personality factors and multiple mini-interviews. Adv Health Sci Educ Theory Pract. 2012;17:377–388
32. Benet-Martínez V, John OP. Los Cinco Grandes across cultures and ethnic groups: Multitrait multimethod analyses of the Big Five in Spanish and English. J Pers Soc Psychol. 1998;75:729–750
33. John OP, Donahue EM, Kentle LK The Big Five Inventory—Version 4a and 54. Technical Report.. 1991 Berkeley, Calif Institute of Personality and Social Research, University of California Berkeley
34. Soto CJ, John OP. Ten facet scales for the Big Five Inventory: Convergence with NEO PI-R facets, self-peer agreement, and discriminant validity. J Res Pers. 2009;43:84–90
35. Chronicles of a Medical Student. . The Multiple Mini Interview: Interview speed dating.
http://medicalstudentchronicles.blogspot.com/2009/09/multiple-mini-interview-interview-speed.html. Accessed May 7, 2012
36. White T. On your mark, get set, interview!
http://med.stanford.edu/ism/2011/january/interview-0110.html. Accessed May 7, 2012
37. Harris G. New for aspiring doctors, the people skills test. New York Times. July 11, 2011:A1
http://www.nytimes.com/2011/07/11/health/policy/11docs.html?pagewanted=all. Accessed May 7, 2012
38. Jerant A, Chapman BP, Duberstein P, Franks P. Is personality a key predictor of missing study data? An analysis from a randomized controlled trial. Ann Fam Med. 2009;7:148–156
39. Srivastava S, John OP, Gosling SD, Potter J. Development of personality in early and middle adulthood: Set like plaster or persistent change? J Pers Soc Psychol. 2003;84:1041–1053
40. Chibnall JT, Blaskiewicz RJ. Do clinical evaluations in a psychiatry clerkship favor students with positive personality characteristics? Acad Psychiatry. 2008;32:199–205
41. Lacorte MA, Risucci DA. Personality, clinical performance and knowledge in paediatric residents. Med Educ. 1993;27:165–169
42. Borges NJ, Savickas ML. Personality and medical specialty choice: A literature review and integration. J Career Assess. 2002;10:362–380
43. Axelson R, Kreiter C, Ferguson K, Solow C, Huebner K. Medical school preadmission interviews: Are structured interviews more reliable than unstructured interviews? Teach Learn Med. 2010;22:241–245
44. Kulatunga-Moruzi C, Norman GR. Validity of admissions measures in predicting performance outcomes: The contribution of cognitive and non-cognitive dimensions. Teach Learn Med. 2002;14:34–42
45. Fernandez A, Wang F, Braveman M, Finkas LK, Hauer KE. Impact of student ethnicity and primary childhood language on communication skill assessment in a clinical performance examination. J Gen Intern Med. 2007;22:1155–1160
46. Hannah A, Murachver T. Gender and conversational style as predictors of conversational behavior. J Lang Soc Psychol. 1999;18:153–174
47. Association of American Medical Colleges.. U.S. medical school applicants and students 1982–1983 to 2011–2012.
https://www.aamc.org/download/153708/data/charts1982to2012.pdf. Accessed May 7, 2012
48. Ones DS, Dilchert S, Viswesvaran C, Judge TA. In support of personality assessment in organisational settings. Pers Psychol. 2007;60:995–1027
49. Knights JA, Kennedy BJ. Medical school selection: Screening for dysfunctional tendencies. Med Educ. 2006;40:1058–1064
50. Knights JA, Kennedy BJ. Medical school selection: Impact of dysfunctional tendencies on academic performance. Med Educ. 2007;41:362–368
