Gerontology & Geriatrics Education, 36:58–78, 2015 Copyright © Taylor & Francis Group, LLC ISSN: 0270-1960 print/1545-3847 online DOI: 10.1080/02701960.2014.966904

Group-Based Differences in Anti-Aging Bias Among Medical Students JORGE G. RUIZ and ALLEN D. ANDRADE Laboratory of E-learning and Multimedia Research, Bruce W. Carter VA Geriatric Research, Education and Clinical Center (GRECC); and University of Miami Miller School of Medicine, Miami, Florida, USA

RAMANAKUMAR ANAM Laboratory of E-learning and Multimedia Research, Bruce W. Carter VA Geriatric Research, Education and Clinical Center (GRECC), Miami, Florida, USA

SABRINA TALDONE University of Miami Miller School of Medicine, Miami, Florida, USA

CHANDANA KARANAM Laboratory of E-learning and Multimedia Research, Bruce W. Carter VA Geriatric Research, Education and Clinical Center (GRECC), Miami, Florida, USA

CHRISTIE HOGUE Laboratory of E-learning and Multimedia Research, Bruce W. Carter VA Geriatric Research, Education and Clinical Center (GRECC); and University of Miami Miller School of Medicine, Miami, Florida, USA

MICHAEL J. MINTZER Laboratory of E-learning and Multimedia Research, Bruce W. Carter VA Geriatric Research, Education and Clinical Center (GRECC); and Herbert Wertheim College of Medicine at Florida, Miami, Florida, USA

Medical students (MS) may develop ageist attitudes early in their training that may predict their future avoidance of caring for the elderly. This study sought to determine MS’ patterns of explicit and implicit anti-aging bias, intent to practice with older people and using the quad model, the role of gender, race, and motivation-based differences. One hundred and three MS completed an online survey that included explicit and implicit measures.

Address correspondence to Jorge G. Ruiz, Laboratory of E-learning and Multimedia Research, Bruce W. Carter VA Geriatric Research, Education and Clinical Center (GRECC), 1201 NW 16th Street, Miami, FL 33125, USA. E-mail: [email protected] 58

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Explicit measures revealed a moderately positive perception of older people. Female medical students and those high in internal motivation showed lower anti-aging bias, and both were more likely to intend to practice with older people. Although the implicit measure revealed more negativity toward the elderly than the explicit measures, there were no group differences. However, using the quad model the authors identified gender, race, and motivation-based differences in controlled and automatic processes involved in anti-aging bias. KEYWORDS ageism, attitudes about older patients, attitudes toward elderly persons, medical students

Ageism is the systematic stereotyping of and discrimination against elderly people (Butler, 1969, 1982, 1990, 2009). Unlike racism, sexism, and other forms of prejudice, ageism and associated anti-aging bias are considered more socially acceptable (Levy & Banaji, 2002; Palmore, 2003). Anti-aging bias seems to be the strongest among all studied social biases (Nosek, Banaji, & Greenwald, 2002) and is due to diverse causes, including sociocultural, demographic, and health care professional attitudes. Anti-aging bias may be preexisting or start early during medical training (Madan, Aliabadi-Wahle, & Beech, 2001; Madan, Cooper, Gratzer, & Beech, 2006). Research demonstrates that anti-aging bias is associated with health care disparities in the care for the elderly (Kane, Priester, & Neumann, 2007; Peake, Thompson, Lowe, & Pearson, 2003; Skirbekk & Nortvedt, 2012). Identifying anti-aging bias early can help medical educators to design bias-reduction interventions that improve the care of older adults. Investigators have studied prejudice using dual process models that include explicit and implicit levels (Devine, 1989). Explicit biases involve deliberative, controlled processes whereas implicit biases involve automatic, mainly unconscious processes. This study used separate measures for explicit bias (e.g., self-report instruments), whereas measures of automatic behavior, such as the Implicit Association Test (IAT), will imply implicit bias (Greenwald, McGhee, & Schwartz, 1998). The evaluation of explicit age bias in medical students (MS) has produced inconsistent findings, with some studies showing negative attitudes (Brooks, 1993; Madan et al., 2001; Perrotta, Perkins, Schimpfhauser, & Calkins, 1981; Reuben, Fullerton, Tschann, & Croughan-Minihane, 1995; Ten Haken, Woolliscroft, Smith, Wolf, & Calhoun, 1995) and others providing evidence of neutral or positive attitudes (Fitzgerald, Wray, Halter, Williams, & Supiano, 2003; Kishimoto, Nagoshi, Williams, Masaki, & Blanchette, 2005; Wilderom et al., 1990). However, self-report may be influenced by socially desirable responding, answering style, interpretations of individual item wording, or limits

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of participant memory (Amodio & Devine, 2006; Dovidio, Kawakami, & Gaertner, 2002; Puhl & Brownell, 2006; Puhl, Schwartz, & Brownell, 2005). Furthermore, in a modern society that promotes egalitarian values and censures prejudice, individuals may avoid acting on their internal prejudices (Plant & Devine, 1998). Individuals holding internal prejudices against an out-group may yield to external pressure and follow the social norm, whereas other individuals may behave in nonprejudicial ways because it is personally important to them. Yet others may do so for internal and external reasons (Plant, Devine, & Brazy, 2003). Whether an individual holds internal or external motivations to behave in a prejudicial way may be associated with different types of self-regulation strategies. In turn, these strategies can produce different effects on the individual’s explicit and implicit forms of prejudice (Devine, Plant, Amodio, Harmon-Jones, & Vance, 2002). These prejudices may manifest in different associated behaviors toward the target out-group (Butz & Plant, 2009). Measurement of implicit bias, on the other hand, may better predict behaviors toward the elderly than self-report measures alone (Bessenoff & Sherman, 2000; Greenwald, Poehlman, Uhlmann, & Banaji, 2009; Roddy, Stewart, & Barnes-Holmes, 2011). The IAT is the best known and most used implicit measure (Greenwald et al., 1998). The IAT evaluates the overall strength of associations between concepts by ascertaining individuals’ latencies in response to categorization tasks (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005; Devine et al., 2002; Greenwald et al., 2002). However, the differentiation between controlled and automatic responses may not be as neat as once believed. In many instances, controlled and automatic processes may operate concurrently (Conrey et al., 2005; Greenwald, Oakes, & Hoffman, 2003). A significant limitation of explicit and implicit measurement tasks is that they may not completely isolate their respective controlled or automatic processes (Conrey et al., 2005). Analysis of IAT responses using the quadruple process model (quad model), a multinomial process (Batchelder & Riefer, 1999), allows us to tease apart automatic and controlled cognitive processes that contribute to performance on the IAT. The quad model has been validated in the field of social bias research (Conrey et al., 2005) and proposes that four cognitive processes determine behavior. Cognitive processes include responses influenced by activation of an association (AC) in response to a relevant stimulus. During IAT tasks, the presentation of an older person may automatically activate a negative evaluation (AC) that influences responses to a stimulus word introduced afterwards. The second determinant is discriminability (D), the accurate detection of a contextually appropriate response. Depending on the IAT trial type, an individual’s automatic inclination may be compatible or incompatible with the correct response determined through discrimination (D) of the target word. If the word is negative, then the response tendency caused by the individual’s automatic evaluation and the response ascertained via discrimination are

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compatible. Engaging in a self-regulatory process such as overcoming bias (OB) represents a third determinant. If the responses during discrimination are compatible, there is no need to overcome bias to generate a correct response. Conversely, if the two response tendencies are incompatible (i.e., an older person prime followed by a positive target word), individuals’ success in overcoming their associations determines whether the automatic association or accurate discrimination drives the response. The last determinant, random guessing (G), reflects a tendency to guide responses in the absence of other available guides. If no association is activated and the correct response cannot be ascertained, participants must guess (G). This study sought to determine medical students’ patterns of explicit and implicit bias toward the elderly, including the role of gender, race, and motivation-based differences. We were particularly interested in (a) whether higher levels of explicit and implicit anti-aging bias are associated with intent to treat older patients, as well as (b) the relationship, according to the quad model of group differences in gender, race, and motivation. Our hypotheses were that (a) higher levels of explicit and implicit anti-aging bias will be associated a lower intent to treat older patients; and (b) stronger activation of positive associations for the young among male, White, and lower internal motivation (IM) medical students. Therefore, we predicted that positive automatic associations (AC) parameters for the young would be significantly higher in medical students who are men, Whites, and those MS with lower IM; (c) stronger overcoming bias parameters in women and medical students with higher IM. We did not have specific predictions regarding race or motivation-based differences in the OB parameter; (d) Moreover, we assumed that there should not be a difference in response biases (parameter G) between the groups. Concerning the D parameter, we had no specific predictions.

METHOD Study Design and Participants This is a cross-sectional study of medical students who completed online self-report questionnaires and the IAT. Participants were drawn from across the four classes attending a Liaison Committee on Medical Education (LCME) accredited medical school in the United States. Medical students from all four classes were recruited to take part in the online study via e-mail announcements. The intervention and data collection took place online between October 25, 2012 and May 30, 2013. Participants received individual logins and passwords by e-mail. Students were able to sign an informed consent form and complete the online questionnaires. The participants could complete the study from any computer with Internet access. Medical students received a $5 voucher as compensation for their participation in the study.

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In 2003, with grant support, this medical school created a competency-based curriculum that shifted the focus from traditional, transfer-of-information pedagogy to a learner-based approach, and documented students’ competency in core areas of geriatrics. The medical school has had a required geriatrics rotation for 3rd-year medical students since the late 1980s; by 2000, this rotation had evolved into a 4-year longitudinal curriculum in which students faced an increasingly challenging set of learning experiences. We obtained full study approval through the institutional review boards.

Participant Characteristics One hundred and three medical students (out of 750) completed the online study. Participants were 50% male (n = 51), 95% medical doctor (MD) track (n = 97) and 5% either MD/master’s in public health (MPH) (n = 5), or MD/doctorate of philosophy (PhD) track (n = 1); 33% first year medical students (MS1) (n = 33), 34% second year medical students (MS2) (n = 35), 21% third year medical students (MS3) (n = 22), and 12% fourth year medical students (MS4) (n = 12). Participants’ age ranged from 20 to 33 (M = 24.81, SD = 2.31). Participants were 65% White (n = 67), 29% Asian (n = 30), and 6% Black (n = 6). Students in this sample were slightly younger that the rest of the student body at this institution (M = 26.20 years), but the gender and racial composition was similar: 52% male (n = 388), White 56 % (n = 423), Asian 22% (n = 168), Black 6% (n = 45), and undeclared 15% (n = 114).

Participant Assessment and Intervention Measures. All consented eligible participants completed online versions of a sociodemographics questionnaire including questions about age, gender, race, ethnic group, and medical school year and the following instruments: 1. Internal Motivation/External Motivation Aging Scale: This validated tool was adapted from Plant and Devine’s (1998) scale to measure racial and obesity biases. The scale was adapted so that statements regarding target individuals (Black or obese people respectively) were changed to “older people.” This scale measures differences in low-prejudice individuals’ regulatory ability, which may be linked to their motivations to respond without prejudice. According to such a framework, nonprejudiced responses are typically driven by a combination of two independent sources of motivation: internal (personal) and external (normative). Participants indicated their agreement with five internal motivation (IM) (Cronbach α = .83) and five external motivation (EM) (Cronbach α = .89) items on 9-point scales ranging from 1 (strongly disagree) to 9 (strongly agree). Participants’ scores on the IM and EM items were averaged such that they ranged from 1 to 9, with higher scores reflecting higher levels of the relevant motivation.

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2. Fraboni Scale of Ageism (FSA): The Fraboni Scale of Ageism measures attitudes toward the elderly, including an individual’s perception of seniors’ residential patterns, cross-generational relations, dependence, cognitive style, personal appearance, and personality. The FSA has 29 items on a 5point Likert-type scale, which uses an operational definition of ageism based on three of Allport’s (1958) five levels of prejudice: antilocution, avoidance, and discrimination (Fraboni, Saltstone, & Hughes, 1990). Higher scores in the FSA indicate more ageism (some questions were reverse scored). The Cronbach α for this study was .85. 3. Intended Practice Patterns with Older Patients (IPPOP): For this study, we adapted items specifically items related to future practice plans from Helton and Pathman (2008) on family medicine resident attitudes towards elderly patients. We developed a 10-item survey on a 5-point Likert-type scale, including questions related to medical students’ future practices with elderly patients. We ensured face validity and content validity of the IPPOP by including two board-certified geriatricians and a gerontologist in developing the instrument items. Lower scores in the IPPOP represent a higher intent to practice with older patients. The Cronbach α for this study was .87. 4. The IAT: Participants completed an online version of the IAT (Inquisit Millisecond Software, Seattle, WA) that asked them to pair the terms old people and young people with positive (a total of 10 words) and negative (a total of 10 words) “affective” attributes, such as unpleasant-pleasant. For most people, it is more difficult to pair old people with positive attributes than it is to pair young people with the same attributes, indicating an implicit bias against old people and a preference for young people (Teachman & Brownell, 2001). The words are combined according to established IAT protocols, with 10 photographs of elderly individuals and 10 photographs of younger individuals. Next, each participant completed an evaluative IAT (Greenwald et al., 1998), in which they paired pleasant and unpleasant words with pictures of old and young men. Participants first completed two 10-trial practice blocks in which they discriminated pleasant from unpleasant words, and young from old faces. The third and fourth blocks were critical, consisting of 20 trials each. Participants were instructed to press one key (I/E) whenever they saw a picture of a young person or a pleasant word, and another key (I/E) whenever they saw picture of an old person or an unpleasant word. The keys used to categorize old and young faces were switched in subsequent blocks. The fifth block was a 10-trial practice block in which participants discriminated old from young faces using the new key assignments. The sixth and seventh blocks were critical, consisting of 20 trials each. Participants were instructed to press one key (I/E) whenever they saw a picture of a young person or an unpleasant word, and

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another key (I/E) when they saw a picture of an old person or a pleasant word. Participants who respond more quickly when “old” shares a key with “unpleasant” (commonly referred to as a “compatible” trial) than when “old” shares a key with “pleasant” (commonly referred to as an “incompatible” trial) are thought to have an implicit preference for young relative to old individuals (Greenwald et al., 1998). Target category and attribute labels remained on the top left and top right of the screen throughout the task, while stimulus pictures and words appeared at the center of the screen. A red X appeared whenever participants made an error, which they were required to correct before moving onto the next trial. Latencies were recorded to the correct response. Participants were instructed to make their classifications as quickly and accurately as possible. The IAT is a timed word classification task and was scored according to the algorithm described by Greenwald, Nosek, and Banaji (2003). In this study, positive IAT d scores indicated stronger associations of negative attributes with old people compared with young people, whereas an IAT d score of 0 indicated no difference in associations with old people compared with young people. IAT d scores were categorized into slight (IAT d score > .15), moderate (IAT d score > .35), or strong (IAT d score ≥ .65) preference for older or younger individuals. We also assessed IAT responses using the quad model by comparing parameter estimates in compatible and incompatible trials. We used Multitree software for multinomial processing tree models (Moshagen, 2010) to estimate the observed probabilities of correct/incorrect responses. Five measures were estimated for each participant: automatic activation of associations (AC) between young/positive and between old/negative; relatively controlled ability to detect the appropriate responses on the task or detection of correct responses (D); ability to overcome automatic associations when they conflict with correct responses (OB); and guessing response bias to press the “pleasant” key (G). The value of .5 indicated random guessing (Figure 1).

Data Analysis We analyzed descriptive statistics, scale reliability, and bivariate correlations for IAT d scores and explicit measures with the IPPOP. We compared IAT d scores and explicit measures with the demographic variables of gender and race (Asian and White), using independent sample two-tailed student t-test. Using IM/EM scales data, MS were assigned to motivation groups on the basis of tertiles, with n = 34 for high and low IM and high and low EM. IM/EM subgroups were assigned based on median splits (high IM-high EM, high IM-low EM, low IM-high EM, and low IM-low EM), as there were insufficient data to complete quad modeling using the tertiles. We compared IPPOP based on the four resulting median split IM/EM subgroups, using oneway ANOVA with IPPOP, IAT d scores, and the FSA. We compared IPPOP,

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Ageist Attitudes in Medical Students Compatible

Left Young/Pos.

Right Old/Neg.

Left Young/Neg.

Right Old/Pos.

+

+

–/+

–

+

+

–

–

Correct response not detected

+

+

–

–

Correct response detected

+

+

+

+

Bias of guessing right key

–

+

–

+

Bias of guessing left key

+

–

+

–

Bias overcome

D

Correct response detected

OB

1-OB

Association activated AC

1-D

Incompatible

Bias not overcome

Stimulus

1-AC

D Association not activated G

1-D Correct response not detected

1-G

FIGURE 1 Quadruple process model (Quad Model) processing tree applied to age (young, old). Note. Parameters with lines leading to it are conditioned on all preceding parameters. Right side of figure shows response [correct (+) or incorrect (−)] to positive and negative words and age categories (young, old) targets as a function of processes and block type (compatible; incompatible). AC = activation of an association; D = detection (correct response); OB = overcoming bias; G = guessing right key; 1-AC = lack of activation of an association; 1-D = failure of detection (correct response); 1-OB = failure to overcome bias; 1-G = guessing left key; Pos. = positive; Neg. = negative.

FSA, and IAT d scores with high (high IM-high EM and high IM/low EM) and low (low IM-high EM and low IM-low EM) IM, as well as high (low IM-high EM and high IM-high EM) and low (low IM-low EM and high IM-low EM) EM using independent sample two tailed Student’s t-test. We then performed a correlation between IAT Test d scores and explicit measures. Next, we calculated parameter estimates of the IAT quad model using error rates on each trial type for each participant. Subsequent analyses using nested χ 2 tests (Hu & Batchelder, 1994) were used to evaluate statistical significance of the automatic associations (AC), detection of correct responses (D), and overcoming biased responses (OB) parameters; difference in strength of the automatic associations parameters; and right-key bias (G) for gender, race (Asian and White), IM (high and low), and EM (high and low). Parameters were tested in separate models, resulting in χ 2 (1 df ) for each parameter. A statistically significant decrement in model fit from the full (hypothesized) to constrained model suggested that the parameters of interest statistically differ from one another.

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RESULTS Explicit Measures We reported descriptive statistics, bivariate correlations, and internal consistency of explicit and implicit measures in Table 1. Respondents’ mean FSA score was 67 (SD = 12), representing a moderately positive perception of older people. There were significant differences between the four IM/EM scales subgroups for FSA (p < .001, partial eta squared = .33), and IPPOP (p = .02, partial eta squared = .09). Post hoc comparisons are reported in Table 2 and reveal that high IM/low EM participants have less negative attitudes than the remaining participants and were more likely to intend to TABLE 1 Descriptive Statistics, Bivariate Correlations With IPPOP, and Internal Consistency of Implicit and Explicit Measures, and Intended Practice Patterns

FSA IMS EMS IPPOP IAT

Mean

SD

Min.

Max.

Med.

No. of items

Range

α

67 6.7 4.9 27.40 .59

12 1.4 1.8 6.58 .46

46 2.4 1.0 10.00 −1.51

106 9.0 9.0 45.00 1.31

68 6.6 5.0 27.00 .67

29 5.0 5.0 10.0

29−145 1−9 1−9 10−50

.85 .83 .89 .87

Note. Med. = median; No. of items = Total number of items in the instrument; Range = scale range; FSA = Fraboni Scale of Ageism; IMS = Internal Motivation Scale; EMS = External Motivation Scale; IPPOP = Intended practice patterns with older patients; IAT: Implicit Association Test.

TABLE 2 Post Hoc Comparison of Intended Practice Patterns With Explicit and Implicit Measures Based on IMS/EMS Subgroups. Groupings Based on Median Splits Motivational differences

FSA Mean SD Count IPPOP Mean SD Count IAT Mean SD Count

High IMS-High EMS

High IMS-Low EMS

Low IMS-Low EMS

Low MS-High EMS

Total

64.00a 8.01a 23a

57.85b 7.69b 27b

71.26c 14.69c 28c

75.56c 7.39c 25c

67.13 12.05 103

26.36a,b 5.37a,b 23a,b

24.70a 7.42a 27a

29.19b 5.96b 28b

29.28b,c 6.36b,c 25b,c

27.40 6.58 103

.597a,b .470a,b 23a,b

.602a,b .380a,b 27a,b

.449a .611a 28a

.741b .290b 25b

.593 .463 103

Note. FSA = Fraboni Scale of Ageism; IMS = Internal Motivation Scale; EMS = External Motivation Scale; IPPOP = intended practice patterns with older patients; IAT: Implicit Association Test. Values in the same row and subtable not sharing the same superscript are significantly different at p < .05 in the two-sided test of equality for column means. Cells with no subscript are not included in the test.

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TABLE 3 Comparison of Practice Patterns, Implicit and Explicit Measures for Demographic Variables: Gender, Medical School Year, and Two Largest Racial Groups Gender

FSA Mean SD Count IMS Mean SD Count EMS Mean SD Count IAT Mean SD Count IPPOP Mean SD Count

Medical school year

Asian versus White

Male

Female

Junior

Senior

Asian

White

71.71a 11.67a 51a

62.55b 10.69b 52b

65.74a 11.78a 69a

69.91a 12.25a 34a

68.90a 12.02a 30a

66.38a 12.16a 67a

6.4a 1.6a 51a

7.0b 1.2b 52b

6.9a 1.4a 69a

6.2b 1.5b 34b

6.9a 1.4a 30a

6.6a 1.4a 67a

5.1a 1.8a 51a

4.8a 1.8a 52a

5.0a 1.8a 69a

4.9a 1.9a 34a

5.0a 1.9a 30a

4.8a 1.8a 67a

.638a .445a 51a

.550a .479a 52a

.602a .414a 69a

.575a .555a 34a

.700a .325a 30a

.550a .518a 67a

29.04a 6.81a 51a

25.72b 5.94b 52b

26.15a 5.82a 69a

29.85b 7.35b 34b

26.93a 6.33a 30a

26.94a 6.45a 67a

Note. FSA = Fraboni Scale of Ageism; IMS = Internal Motivation scale; EMS = External Motivation Scale; IPPOP = intended practice patterns with older patients; IAT: Implicit Association Test. Values in the same row not sharing the same superscript are significantly different at p < .05 in the two-sided test of equality for column means.

practice with older people in future. Low IM/low EM had the most negative attitude (p < .001, Cohen d = 1.37) and was less likely to intend to practice with older people (p = .01, Cohen d = .82) in comparison to high IM/EM group. Female MS had less negative attitudes on FSA (p < .001, Cohen d = .81), higher IM (p = .04, Cohen d = .42) and were more likely to intend to care for older people than their male peers (p = .01, Cohen d = .51). There were no differences in negative attitudes between early MS (first and second years) and more senior MS (p = .09). Early MS exhibited higher IM (p = .02, Cohen d = .48), and were more likely to intend to care for older people (p = .007, Cohen d = .42) than more senior MS. Mean group differences are presented in Table 3. There were no differences between Asian and White MS in FSA (p = .34), IM (p = .50), EM (p = .64), and IPPOP (p = .99). We identified that participants with low IM were more likely to have negative attitudes on FSA (p < .001, Cohen d = 1.41) and were less likely to intend to care for older people on IPPOP (p = .003, Cohen d = .60).

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Implicit Measures The IAT d scores revealed that a majority of participants (90%) showed a preference for young in comparison with old people: the preference was strong in 52% (n = 53), moderate in 27% (n = 28), and slight in 11% (n = 12). Only 10% of participants showed a preference for older people: neutral (n = 3), slight (n = 2), moderate (n = 2), or strong (n = 3). There were neither gender-based (p = .13), race-based (p = .66), nor motivation-based (IM p = .97, EM p = .6) differences in IAT d scores. The quad model fit the data, χ 2 (6) = 11.91, p > .05. Means and confidence intervals for the parameter estimates suggested a greater affinity for automatic associations for young/positive than old/negative (AC); a high D estimate representing a high correct response rate; G scores were indicative of a significant amount of guessing; and an overcoming bias parameter (OB) score of .49. Group differences in gender, race, and motivation using the quad model can be seen on Table 4. Motivational differences. There were no differences between participants with low EM and high EM,  χ 2 = .67 (df = 1), p = .41 for young/positive automatic associations; and between low EM and high EM participants for old/negative automatic associations,  χ 2 = 1.21 (df = 1), p = .27. There were no differences in detection of correct responses between low EM and high EM participants,  χ 2 = 2.29 (df = 1), p = .13, and no differences in overcoming bias parameters between high EM and low EM participants,  χ 2 = 1.88 (df = 1), p = .17. Random guessing (G estimates) were higher for low EM rather than high EM participants,  χ 2 = 6.63 (df = 1), p = .01. As the high IM-low EM subgroup appeared to be the least ageist in most explicit measures, we compared this group with the remaining participants. High IM-low EM participants favored young/positive automatic associations (M = .07, 95% confidence interval [CI][.03, .11]) significantly less than the remaining participants, M = .09, CI [.06, .12],  χ 2 = 19.51 (df = 1), p < .001. There were no differences in old/negative automatic associations between high IM-low EM participants (M = .01, CI [.001, .03]) and the other participants, M = .02, CI [.001, .03],  χ 2 = .01 (df = 1), p = .9. There were no differences in detection of correct responses (D estimates) between high IMlow EM participants (M = 0.90, CI [.87, .92]) and the other participants, M = .89, CI [.87, .91],  χ 2 = .01 (df = 1), p = .99. Random guessing (G estimates) was higher for high IM-low EM participants (M = .58, CI [.43, .73]) than the remaining participants, M = .50, CI [.41, .50],  χ 2 = 19.51 (df = 1, p < .001). High IM-low EM participants were more likely to overcome bias (OB parameter) (M = .61, CI [.11, .99]) than the remaining participants (M = .38, CI [.07, .69],  χ 2 = 19.51, df = 1, p < .001).

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Overall (n = 103) .13 .05 .10 .87

(.07−.18)1 (.01−.10)1 (.01−.05)1 (.84−.90)1

Asians (n = 30) (.07−.14)2 (.00−.05)2 (.35−.92)2 (.90−.93)2

White (n = 67) .11 .02 .64 .92

Race

.13 (.10−.17) .05 (.02−.07)a .53 (.33−.73)a 85 (.83−.86)a

Low (n = 34) .04 .03 .94 .92

(.01−.06)b (.01−.05)b (.77−1.00)b (.91−.94)b

High (n = 34)

Internal motivation

(.04−.09) (.01−.06) (.12−.88) (.86−.90)

.07 .03 .48 .90

(.04−.09) (.004−.05) (.04−.77) (.88−.90)

High (n = 34)

External motivation Low (n = 34) .07 .04 .50 .88

Note. AC = association activation, OB = overcoming biased associations, D = detection, G = guessing. Column estimates with differing superscripts a, b or 1, 2 indicate significant difference (p < .05) between quad model parameter estimates.

(.05−.13)b (.02−.08)b (.55−1.00)b (.87−.91)

Female (n = 52)

(.09−.18)a .09 (.01−.03)a .05 (.21−.79)a .80 (.87−.92) 0.89

Male (n = 51)

AC - Young/Pos. .10 (.07−.13) .13 AC - Old/Neg. .02 (.00−.05) .01 OB .49 (.18−.79) .50 D .90 (.45−.61) 0.90

Parameter

Gender

TABLE 4 Quad Model Parameter Estimates for the Young—Old IAT (Gender, Race and Motivation)

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Correlations We found that the FSA (r = .38, p < .001) and the IM (r = –.33, p = .01) showed significant correlations with the IPPOP. FSA also show a significant correlation with the IM (r = –.6, p < .001). The EM and IAT d scores did not show significant correlations with other measures.

DISCUSSION Medical students in this study demonstrated moderately positive explicit attitudes toward older people. There were, however, important individual differences in motivation between diverse groups of participants. Students with stronger IM demonstrated the weakest anti-bias attitudes and a stronger intent than students with lower motivation to practice with older people. Female MS demonstrated less ageist, anti-aging bias, higher IM, and stronger intentions to practice with older people. As other investigators have found (Nosek et al., 2002), there was a clear dissociation in this study between explicit and implicit measures. Most MS in the IAT demonstrated a strong preference for younger people. The IAT scores masked important group differences in race, gender, and motivation. Looking at the contribution of controlled or automatic processes to age-related bias using the quad model, we found significant gender, race, and motivation-based differences in this sample of MS. Consistent with other studies, which included mostly college students (Allan & Johnson, 2008; Bodner, Bergman, & Cohen-Fridel, 2012; Fraboni et al., 1990; Kalavar, 2001; Kite & Johnson, 1988; Rupp, Vodanovich, & Crede, 2005), female MS in this study displayed lower explicit anti-aging bias and a stronger intent than male students to practice with older patients. However, though men demonstrated increased associations favoring the young, women showed fewer favorable associations for the old. Ageist societal views coupled with sexist values associating female beauty with youthfulness may negatively affect elderly women (Hurd, 1999). Women’s internalization of these stigmas may explain our female participants’ more negative associations for the old. On the other hand, women in this study were more likely than men to overcome these associations. Findings in a large series of IAT responses have consistently shown that women display lower implicit anti-aging bias than men (Nosek et al., 2007). Together with explicit measures, this data suggests the strength of gender differences in age bias. These differences are not restricted to age bias occurring in other social categories (Nosek et al., 2007). Using the quad model, we have gone further in demonstrating that, despite no differences in the IAT d scores, there are clear gender differences in self-regulatory responses. Using IAT stimulus, functional magnetic resonance imaging (MRI) studies have shown

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evidence of greater activity within the anterior cingulate in response to incongruent versus congruent trials (Chee, Sriram, Soon, & Lee, 2000; Q. Luo et al., 2006). Investigators have elucidated the role of the anterior cingulate in conflict monitoring and control of goal-directed behavior (Botvinick, Cohen, & Carter, 2004; Botvinick, Nystrom, Fissell, Carter, & Cohen, 1999; Bush, Luu, & Posner, 2000; Kerns et al., 2004), (i.e., the OB responses in the quad model). Neuroscience-based evidence points to morphologic genderbased differences in the anterior cingulate. Morphologic changes in the brain show that, compared to men, women have a more symmetric pattern distinguished by decreased fissurization of the left anterior cingulate (Yucel et al., 2001). These neural differences may explain the differences in anti-aging bias between men and women. Contrary to the prevailing notion that younger individuals in Asian cultures revere older people (i.e., filial piety) (Liu, Ng, Weatherall, & Loong, 2000; Ng, 1998), studies have shown evidence of anti-aging bias in this group (Harwood et al., 1996; Luo, Zhou, Jung Jin, Newman, & Liang, 2013). Compared with American college students, Chinese students showed more ageist attitudes (Luo et al., 2013). Asian nurses in the United States showed more negative age attitudes than White or Hispanic nurses (Lookinland & Anson, 1995). In the largest online IAT series ever undertaken, Asian individuals demonstrated the strongest implicit preferences for the young as compared with other races (Nosek et al., 2007). We found no racial differences in this study with explicit and implicit measures. However, the similarity in IAT scores masked race-based differences in implicit processes. Asians activated more positive associations for the young and negative associations for the old. Furthermore, Asians were not only less able to overcome these associations than White students, but also showed a lower detection of appropriate responses. These negative attitudes may be related to their dislike of disabled individuals (Saetermoe, Scattone, & Kim, 2001), whom they may often perceive as older. The quad model provides an explanation for the processes involved in the observed bias: besides having more negative age associations, Asian students may also be less able to self-regulate there biased responses. This is the first study that has examined motivation-based individual differences in age bias research. Experts in the field of racial bias research have implicated implicit biases as major factors in the maintenance of prejudice and discrimination (Devine, 1989; Dovidio & Gaertner, 2004). As predicted, medical students in the high IM/low EM group were more likely to demonstrate lower anti-aging bias than the other groups as well as the strongest intent to practice with older patients. On the other hand, medical students in the low IM groups (high and low EM) demonstrated the weakest intent. Higher IM groups were more likely to intend to practice with older people. Individuals motivated to eliminate their entrenched prejudice have first to develop an awareness of their own biases, show concern for the behavioral

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consequences of their biased responses, know when those responses are likely to ensue, and then be able to substitute them with more egalitarian responses (Devine, Forscher, Austin, & Cox, 2012; Plant & Devine, 2009). Although we found no differences among the motivation subgroups in the IAT scores, once again the quad model revealed important motivation-based differences. Higher IM students were less likely to favor the young or display negative attitudes toward the old when compared to lower IM students. Higher IM students were more likely to better detect appropriate responses and overcome age bias than those students with low IM. These findings are consistent with previous studies in the field of racial bias (Amodio, Harmon-Jones, & Devine, 2003; Devine et al., 2002; Gonsalkorale, Sherman, Allen, Klauer, & Amodio, 2011). In the field of racial bias, IM provides the needed drive to control negative associations at the implicit level. Individuals with higher IM may train themselves to reduce their negative responses and overcome their negative associations, thereby improving their self-regulatory responses (Calanchini, Gonsalkorale, Sherman, & Klauer, 2013). We were not able to further confirm these findings with the high EM/low EM subgroup, supposedly the least biased among the four motivation subgroups. Although this latter group demonstrated less positive young associations and superior ability to overcome bias as observed in the larger IM group (high and low EM), we were not able to show differences in either negative old associations or their response detection. Our explanation lies in the smaller sample. This data suggest the importance of considering motivation-based individual differences when examining anti-aging bias (Devine et al., 2002; Plant & Devine, 1998; Plant et al., 2003). Strengths of this study are the use of explicit and implicit measures, and the inclusion of students from all 4 years of medical school. There are, however, some limitations. This sample of students was relatively small, at a single medical school, and lacking a sufficient number of African American students, which precluded us from studying other racial differences. On the other hand, this school’s racial composition is similar to that of other U.S. medical schools. These findings provide preliminary evidence of gender, race, and motivation-based differences in implicit age bias processes. All these findings need to be confirmed and investigated in larger studies using the quad model and including representative samples with gender, race, and motivation diversity in medical students and other health care professional students in other geographic locations and in different cultural environments.

Practice Implications There is strong evidence that implicit bias may be malleable in response to a variety of manipulations (Blair, 2002). One such strategy, counterprejudicial training, may reduce individuals’ implicit bias (Calanchini et al., 2013; Gawronski, Deutsch, Mbirkou, Seibt, & Strack, 2008; Kawakami,

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Phills, Steele, & Dovidio, 2007; Kawakami, Dovidio, & van Kamp, 2005). Determining the contributions of individual processes to implicit bias as identified by the quad model may assist investigators in developing targeted bias-reduction strategies. Conceivably, bias-reduction strategies would address individuals’ specific processing deficits. One study of racial bias using the quad model demonstrated reductions in biased associations and improvement in the detection of appropriate, nonbiased responses using targeted counterprejudicial training (Calanchini et al., 2013). Identification of implicit processes involved in anti-aging bias with the quad model may similarly assist investigators in the development of bias-reduction strategies in undergraduate medical curricula and help educators test the effectiveness of anti-aging bias-reduction interventions in improving health care professionals’ attitudes. Considering gender, race, and motivation differences will be important in the targeting of the bias-reduction strategies to specific groups. This research may further clarify the issues involved in the care of older patients and lead to bias-reduction efforts that lead to improved care for older adults in behavior but also in health care professional interest in working with elderly patients.

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