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Does Mentoring Matter? A Multidisciplinary Meta-Analysis Comparing Mentored and Non-Mentored Individuals

Lillian t. eby.

University of Georgia

Tammy D. Allen

University of South Florida

Sarah C. Evans

University of Hong Kong

David DuBois

University of Illinois-Chicago

The study of mentoring has generally been conducted within disciplinary silos with a specific type of mentoring relationship as a focus. The purpose of this article is to quantitatively review the three major areas of mentoring research (youth, academic, workplace) to determine the overall effect size associated with mentoring outcomes for protégés. We also explored whether the relationship between mentoring and protégé outcomes varied by the type of mentoring relationship (youth, academic, workplace). Results demonstrate that mentoring is associated with a wide range of favorable behavioral, attitudinal, health-related, relational, motivational, and career outcomes, although the effect size is generally small. Some differences were also found across type of mentoring. Generally, larger effect sizes were detected for academic and workplace mentoring compared to youth mentoring. Implications for future research, theory, and applied practice are provided.

Across areas of research, scholars agree that mentoring can be associated with a wide range of positive outcomes for protégés. Mentoring has been discussed as a strategy for positive youth development and as a deterrent of risky youth behavior ( DuBois & Karcher, 2005 ), as a way to improve the academic adjustment, retention, and success of college students ( Johnson, in press ), and as a means to facilitate career development among employees ( Kram, 1985 ). Despite the widespread study of mentoring and its prevalence in community, academic and organizational contexts, research has progressed within its own disciplinary silos. As a consequence, there is little cross-disciplinary communication among mentoring scholars. There are also no quantitative reviews of the mentoring literature as a whole, even though the same basic assumption applies to all types of mentoring. That is, when a more experienced or senior individual (the mentor) takes an interest in and encourages a less experienced or disadvantaged individual (the protégé), the protégé will benefit ( Jacobi, 1991 ; Kram, 1985 ; Rhodes, 2005 ).

To spark mentoring researchers to think more broadly about the potential role of mentoring in protégés’ lives and to advance mentoring theory, a comprehensive multi-disciplinary meta-analysis was conducted. Our primary objective was to answer the question, “Looking across different areas of mentoring scholarship, does mentoring matter, and if so, how much?” This is an important question because the popular press makes strong claims about the importance of mentoring and both public and private funds are used to support many different types of mentoring initiatives ( Rhodes, 2005 ). We were also interested in documenting whether or not there are differences in how much mentoring matters across protégé outcomes. For example, does mentoring have a stronger relationship with protégé attitudes (e.g., attitudes toward school, satisfaction with college, job satisfaction), protégé behaviors (e.g., grades in school, deviant behavior, job performance), or protégé motivational variables (e.g., aspiration level, time spent on educational pursuits, career commitment)? This information has implications for theory development and refinement. It may also alert practitioners as to the protégé outcomes that may be most likely affected by mentoring when designing formal programs. Finally, we were interested in examining whether mentoring outcomes vary by the type of relationship (youth mentoring, workplace mentoring, academic mentoring). This will provide a more fine-grained assessment of the conditions under which mentoring matters the most.

Overview of the Mentoring Literature

Because individuals may experience mentoring at various life stages, it is not surprising that there are three distinct streams of mentoring scholarship: youth mentoring, academic mentoring, and workplace mentoring. Youth mentoring involves a relationship between a caring, supportive adult and a child or adolescent ( Rhodes, 2002 ). Youth mentoring assumes that supportive relationships with adults are important for personal, emotional, cognitive, and psychological growth ( Ainsworth, 1989 ; Rhodes, 2002 ). Academic mentoring typifies the apprentice model of education where a faculty member imparts knowledge, provides support, and offers guidance to a student protégé on academic (e.g., classroom performance) as well as non-academic (e.g., personal problems, identity issues) issues ( Jacobi, 1991 ). This type of mentoring may facilitate psychological adjustment and foster a sense of professional identity ( Austin, 2002 ). Finally, workplace mentoring occurs in an organizational setting and the purpose is the personal and professional growth of the protégé ( Kram, 1985 ). The mentor may be a supervisor, someone else within the organization but outside the protégé’s chain of command, or an individual in another organization ( Eby, 1997 ).

Several narrative reviews of the youth, academic, and workplace mentoring literature exist. Some narrative reviews summarize research findings associated with youth, academic or workplace mentoring in a particular area, such as diversity (e.g., Ragins, 2002 ), formal mentoring relationships (e.g., Miller, in press ), or naturally occurring mentoring relationships (e.g., Mullen, in press ; Zimmerman, Bingenehimer, & Behrendt, 2005 ). Other reviews focus on a specific type of mentoring (e.g., academic, workplace) more broadly (e.g., Jacobi, 1991 ; Wanberg, Welsh, & Hezlett, 2003 ). Several quantitative reviews also exist. This includes quantitative reviews of formal youth mentoring ( DuBois, Holloway, Valentine & Cooper, 2002 ), academic mentoring ( Dorsey & Baker, 2004 ; Sambunjak, Straus, & Marusic, 2006 ), and workplace mentoring ( Allen, Eby, Poteet, Lentz, & Lima, 2004 ; Underhill, 2006 ). Collectively these represent important efforts to synthesize the literature. However, there are no reviews that incorporate diverse areas of mentoring scholarship or compare mentoring outcomes across youth, academic and workplace mentoring. The present study addresses this issue.

Hypothesized Effects of Mentoring on Outcomes

We expect a wide range of outcomes to be related to mentoring. This includes behavioral, attitudinal, health-related, relational, motivational, and career outcomes.

Behavioral outcomes

Mentoring is often discussed as a means to increase desirable behavior (e.g., academic performance, job performance) and decrease undesirable behavior (e.g., school drop-out, substance use). In fact, formal mentoring programs for youth and college students often target “at risk” individuals (cf. Campbell, in press ; Rhodes, 1994 ). The hope is that mentoring will deter negative outcomes such as drug use, teen pregnancy, college drop-out, and academic failure while simultaneously encouraging alternative positive behaviors. Another way that protégé behavior may be affected is through instrumental assistance provided by mentors (e.g., helping to publish articles, complete homework, successfully finish work tasks) ( Cohen & Willis, 1985 ). This leads us to propose:

Hypothesis 1: Mentoring is associated with positive behavioral outcomes.

Attitudinal outcomes

Mentoring may also have a positive effect on protégé attitudes. For instance, it is presumed that protégés will develop positive attitudes toward the activity that they engage in with their mentors. This might include activities associated with school ( Blinn-Pike, in press ; Tennenbaum, Crosby, & Gliner, 2001 ), graduate training ( Johnson, Koch, Fallow, & Huwe, 2000 ), or job assignments ( Chao, Walz, & Gardner, 1992 ). Having a mentor may also foster psychological attachment to the context in which the relationship is embedded, such as one’s school, university, or organization (e.g., Payne & Huffman, 2005 ). Thus, we expect:

Hypothesis 2: Mentoring is associated with positive attitudinal outcomes.

Health-related outcomes

Another facet of the mentoring relationship involves the provision of emotional and other forms of health-related support to the protégé. A mentor may listen and offer advice during times of stress or provide counseling on personal or job-related issues ( Kram, 1985 ). Mentors can also enhance overall well-being by challenging protégés’ negative self views ( Rhodes, 2002 , 2005 ) which may enhance protégé self-confidence or self-esteem ( Johnson, in press ). Furthermore, mentors may be able to promote protégé physical health by engaging in activities such as exercise with the protégé or by facilitating protégé access to health services ( DuBois & Silverthorn, 2005 ). As such we propose:

Hypothesis 3: Mentoring is associated with positive health-related outcomes.

Relational outcomes

Mentoring also may enhance interpersonal relationships with parents, siblings, and peers ( Rhodes, 2002 , 2005 ). For example, mentors may help protégés figure out appropriate strategies to deal with interpersonal problems at work, home or school. Moreover, the experience of a trusting, close relationship with a mentor may lead the protégé to develop positive expectations about interpersonal relationships with others ( Rhodes, Grossman, & Rensch, 2000 ) which in turn may promote positive relationships. This leads us to propose:

Hypothesis 4: Mentoring is associated with positive relational outcomes.

Motivational outcomes

Protégé motivation and involvement may also be influenced by mentoring. Role modeling can expose protégés to educational and social opportunities, which may open their eyes to different possibilities and motivate them to seek out new experiences ( Spencer, in press ). Motivation also may be enhanced by helping protégés set achievable goals and realize personally relevant outcomes ( Ramaswami & Dreher, in press ). Moreover, mentors may help protégés stay focused on tasks and steer them away from superfluous activities ( Bearman, Blake-Beard, Hunt & Crosby, in press ). Based on this, we propose:

Hypothesis 5: Mentoring is associated with positive motivational outcomes.

Career outcomes

Finally, mentoring relationship may promote career success. Mentors can impart specific knowledge and expertise which contributes to protégé learning and skill development ( Kram, 1985 ; Johnson, in press ; Mullen, in press ). Mentors can also facilitate professional networking by introducing protégés to influential individuals within academic and organizational contexts ( Kram, 1985 ; Tennenbaum e al., 2001 ). These important career contacts can in turn lead to career success in terms of salary, promotions, and job offers. With youth or college students, mentors also may introduce protégés to different possible careers and help them to explore those, thus enhancing their development in this area. Thus, we propose:

Hypothesis 6: Mentoring is associated with positive career outcomes.

Differences in Youth, Academic and Workplace Mentoring

Although similar in some respects, youth, academic, and workplace mentoring also differ. One salient difference is the developmental stage of the protégé. Developmental theories suggest that people progress through relatively orderly periods of transition marked by unique challenges ( Erikson, 1963 ; Levinson, Darrow, Klein, Levinson, & McKee, 1978 ). These developmental transitions represent critical turning points and if not navigated successfully there are psychological and social consequences ( Erikson, 1963 ). From middle childhood to adolescence the primary developmental issues involve learning how to cultivate healthy peer relationships, master academic challenges, and develop a sense of personal responsibility ( Erikson, 1963 ; Spencer, in press ). In early adulthood the transitions revolve around psychological and physical separation from one’s parents, learning to develop close emotional bonds with non-family members, and identity development ( Erikson, 1963 ; Levinson et al., 1978 ). By the time one enters the workforce, the transition generally focuses on developing a stable occupational self-image and finding a niche for oneself in society ( Levinson et al., 1978 ).

Mentoring at different developmental stages also tends to serve different functions or purposes. Youth mentoring is often aimed at reducing risky behavior or improving social and academic functioning ( DuBois & Karcher, 2005 ). Academic mentoring tends to target student retention, academic performance, and adjustment to college life ( Jacobi, 1991 ). Finally, workplace mentoring aims to enhance employees’ personal and career development ( Kram, 1985 ). Based on the unique developmental transitions individuals face across the lifespan and the varying purposes of different types of mentoring we propose the following research question:

Are there differences in protégé outcomes when comparing youth, academic, and workplace mentoring?

Literature Search

A comprehensive search of articles published from 1985–2006 was conducted to identify articles examining differences between protégés and non-protégés on a wide range of outcomes. PsycINFO, Business Source Premier, ERIC, Educational Abstracts, Medline, PubMed, Sociological Abstracts, and Social Sciences Abstracts were searched to identify relevant articles Search terms included “mentor” and all derivations of this word (e.g., mentoring, mentored), “Big Brother”, “Big Sister”, “non-parental adult” and “buddy” in a relevant search field (i.e., title, abstract, keyword, descriptor, major topic). We also cross-referenced quantitative reviews ( Allen et al., 2004 ; Dorsey & Baker, 2004 ; DuBois et al., 2002 ; Sambunjak et al., 2006 ; Underhill, 2006 ), narrative reviews (e.g., Jacobi, 1991 ; Wanberg et al, 2003 ) and other major compendiums (e.g., Allen & Eby, in press ; DuBois & Karcher, 2005 ) to identify additional articles. Finally, the websites of several nationwide formal mentoring programs (e.g., Big Brothers/Big Sisters) as well as organizations that routinely evaluate or fund research in the area of mentoring (Public/Private Ventures) were searched for articles and reports. Unpublished research such as conference papers, dissertations and theses were excluded. Because of this we conducted a file drawer analysis. The fail safe N (FSN) represents the number of missing studies averaging null results that would be needed to reduce the effect size to a specified level ( Rosenthal, 1984 ). In the present study we used an alpha level of p = .05.

Eligibility Criteria

The initial search process yielded 15,131 articles and reports. To be considered for inclusion the study had to compare mentored and non-mentored individuals on an individual-level outcome (e.g., academic success, drug use, work attitudes). The study also had to be written in English and quantify the relationship between mentoring and the outcome using a statistic that could be converted to a product-moment correlation coefficient (e.g., d -statistic, t -statistic, 2×2 contingency table, chi square with 1 df ). For studies that met the inclusion criteria but did not report usable statistics (e.g., multivariate findings only), we attempted to obtain such data by contacting the study authors. Individual studies also had to meet all of the following criteria:

• The study had to involve youth, academic, or workplace mentoring.
• Neither protégés nor mentors suffered from a major physical or psychological disability (e.g., studies of seriously cognitively impaired individuals were excluded).
• The study involved a focus on traditional one-on-one non-parental mentoring relationships. As such, studies focusing exclusively on peer, group/team, or reverse mentoring were excluded. Also excluded were studies focusing exclusively on parents as mentors, professional caregivers or specialists as mentors, and social support from teachers for youth.
• For intervention studies, mentoring had to be the sole or primary intervention (e.g., interventions that included academic counseling, special coursework, financial aid, and mentoring to improve student retention were excluded).
• Research on teacher induction programs, on-the-job training, and internship programs was excluded because these studies do not necessarily involve one-on-one mentoring.

One hundred and twelve studies and reports met all of the eligibility criteria. Three studies included multiple samples, for a total of 116 independent samples for the meta-analysis. If authors published different studies from the same dataset or a smaller sub-set of the same dataset, only the effect size based on the larger sample size was included. A full list of the studies included in the present meta-analysis is available from the first author.

Operationalization of Constructs

Mentoring was operationalized as non-mentored (coded 1) or mentored (coded 2). As such, positive correlations indicated that being mentored was associated with a higher level of each criterion variable (e.g., more favorable career attitudes, higher self-esteem).

Similar to other published meta-analyses ( Ng, Eby, Sorensen, & Feldman, 2005 ; Valentine, DuBois, & Cooper, 2004 ) variables that were conceptually similar were combined. This was necessary in order to draw general conclusions about the relationship between mentoring and protégé outcomes across different types of mentoring.

Table 1 lists the six broad categories of outcomes examined. Within each category we list the specific outcomes examined and examples of how these outcomes were operationalized. Some of the outcomes listed in Table 1 are applicable across different types of mentoring (e.g., withdrawal behavior, motivation/involvement) whereas other outcomes are more specific to a particular type of mentoring (e.g., deviance was examined exclusively in studies of youth mentoring). If two (or more) effect sizes from the same study were combined into an outcome category, the effect sizes were averaged so that each sample only contributed one effect size ( DuBois et al., 2002 ; Underhill, 2006 ).

Outcomes of Mentoring

Grouping variable

The type of mentoring relationship was also coded so that it could be examined so that we could examine whether the relationship between mentoring and protégé outcomes varied across youth, academic, and workplace mentoring. The 166 independent samples were coded into one of three categories: youth mentoring (n=40, 34.5%), workplace mentoring (n=53, 45.7%), academic mentoring (n=23, 19.8%). Youth mentoring was defined as a naturally occurring (informal) or formally arranged (e.g., Big Brother/Big Sister) relationship between a non-parental adult and a child, adolescent, or young adult ( Blinn-Pike, in press ). Academic mentoring studies were those that examined relationships among undergraduate or graduate students and teachers or faculty members in community colleges, four year colleges, and universities ( Johnson, in press ) (including medical schools and nursing programs). Studies were coded as workplace mentoring if they focused on formal or informal mentoring relationships between working adults in an organizational setting ( Allen et al., 2004 ).

Meta-Analysis Procedure

The 15,131 articles were screened by two of the study authors. The first and second authors were responsible for coding all studies included in the meta-analysis. These two coders independently double-coded articles until they reached over 90% agreement. After reaching over 90% agreement each person single-coded his or her assigned articles. Spot checking throughout the coding process revealed minimal coding errors. Coding discrepancies were resolved through re-examination of the data and when necessary, discussion. The effect size used in the current analysis was the product-moment correlation coefficient.

Hunter and Schmidt’s (1990) meta-analysis technique was used. To compute a meta-analytic correlation at least three studies were required. Each correlation was first corrected for unreliability in the measurement of the outcome variable. If coefficient alpha was not reported for a study we used the average coefficient alpha for the other studies in that outcome category, as is commonly done in meta-analysis (e.g., Ng et al., 2005 ). Some outcomes did not require disattenuation (e.g., organizational turnover, number of days skipped). Next, the sample size weighted correlation was calculated. A corrected correlation was judged to be significant at alpha=.05 when the 95% confidence interval did not include zero.

Sub-Group Analyses

The Q statistic ( Hedges & Olkin, 1985 ) indicated if there was sufficient variability in each meta-analytic effect size to warrant a search for sub-group differences. For effect sizes associated with a significant Q statistic, meta-analytic correlations were computed separately for studies focusing on youth, academic, and workplace mentoring ( Hunter & Schmidt, 2000 ). As with the primary analyses, a minimum of three studies was necessary to compute sub-group meta-analytic correlations. To determine if the effect sizes associated with a particular mentoring-outcome relationship differed significantly across youth, academic, and workplace mentoring, the 95% confidence intervals for each effect size were examined. Non-overlapping confidence intervals provides evidence of significant sub-group differences (cf. Ng et al., 2005 ).

Table 2 provides the results of the meta-analysis of outcomes associated with mentoring. For each relationship we report the total sample size cumulated across studies included in the analysis of that relationship ( N ), number of studies included in the analysis of that relationship ( k ), sample size weighted corrected correlation ( r c ), standard deviation of the r c ( SD c ), the upper and lower 95% confidence interval (95% LCI, 95% UCL), the Q statistic, and the Fail-Safe N. We used the Cohen’s (1988) conventional standards for interpreting correlation-based effect sizes as small (absolute value of .10 to .23), medium (absolute value of .24 to .36) and large (absolute value of .37 or higher). For four outcomes we encountered studies with sample sizes over 3,000 ( Bhatta & Washington, 2003 ; Brashear, Bellinger, Boles, & Barksdale, 2006 ; Clotfelter, 2001 ). We computed effect sizes with and without these large samples.

Fixed-Effects Method Meta-Analytical Relationships between Mentoring and Outcomes

Note. N = cumulative sample size; k = number of studies cumulated; r c = sample-size weighted corrected correlation; SDc = standard deviation of r c ; LCI = lower bound of confidence interval; UCI = upper bound of confidence interval; Q = Q statistic.

An important consideration is whether to adopt a fixed-effects or random-effects meta-analytic model. These methods differ in terms of whether the error term is considered to be homogeneous or heterogeneous across studies ( Hedges & Vevea, 1998 ). With fixed-effects methods, the effect sizes in the population are unknown constants but are fixed, presumed to be the same for all studies included in the meta-analysis. With random-effects methods effect sizes are assumed to vary randomly from study to study and are therefore only a sample of all possible studies that exist on a topic ( Hunter & Schmidt, 2000 ). The random-effects method is generally preferred since it allows generalizations beyond the studies included in a meta-analysis ( Field, 2001 ). However, when fewer than 30 effect sizes are used in calculating meta-analytic correlations, random-effects methods have some serious limitations (i.e., low power to detect small effect sizes, inflated Type I error) ( Field, 2001 ). These problems are exacerbated with the Hunter and Schmidt technique ( Field, 2001 ). Thus, meta-analytic correlations were computed using both fixed-effects (see Table 2 ) and random-effects (see Table 3 ).

Radom-Effects Method Meta-Analytical Relationships between Mentoring and Outcomes

Hypotheses 1–6 were supported. Regardless of the meta-analytic method used (fixed- or random-effects), mentoring was significantly related to favorable behavioral, attitudinal, health-related, interpersonal, motivational, and career outcomes (note that negative correlations with withdrawal behavior, withdrawal intentions, deviance, substance use, and psychological stress & strain indicate more desirable outcomes). The only exception was the non-significant effect size associated with psychological stress & strain when estimated using a random-effects method (see Table 3 ). The largest effect sizes were between mentoring and helping others (large sample removed), school attitudes, and career attitudes. The smallest (but still statistically significant) effect sizes were between mentoring and psychological stress & strain (when estimated using fixed-effects only), career recognition & success, deviance, and self-perceptions. All of the effect sizes were small in magnitude. In most cases our Fail-Safe N analysis indicated that a substantial number of null result studies would need to be added to bring significance to p = .05. However, in some cases only a small number of studies (e.g., school attitudes) or even none (psychological stress & strain) would be required. In such cases, confidence in the stability of the observed effect is questionable and the results should be viewed with caution (Rosenthal, 1979).

Sub-Group Difference Results

Examining the Q statistics in Tables 2 and ​ and3 3 provides mixed evidence with respect to moderation. When using a fixed-effects method several Q statistics are significant. In contrast, when using a random-effects method none of the Q statistics are significant. Due to the concern over using random-effect methods with fewer than 30 ks ( Field, 2001 ), we proceeded with the sub-group analysis for those outcomes in Table 2 that yielded a significant Q statistic using the fixed-effect method. There is evidence of sub-group differences for all of the mentoring-outcome relationships except withdrawal intentions, substance use, and skills/competence development. Therefore, except for the outcomes just noted, we conducted sub-group analyses by type of mentoring relationship (youth, academic, workplace) if there was an adequate number of studies ( k ≥ 3) to do so. For some outcomes (e.g., performance) there were adequate sub-sample sizes to compare all three types of mentoring. In other situations only two of the three types of mentoring could be examined (e.g., school attitudes). Because the effect sizes associated with the three large studies was not appreciably different from the total sample and in all cases the inclusion of the three samples led to more conservative estimates (smaller effect sizes), these studies were included in the sub-group analyses, where appropriate.

Table 4 reveals several patterns of results across different types of mentoring. First, with regard to behavioral outcomes, all three types of mentoring demonstrated significant effect sizes for performance. However, as evidenced by the nonoverlapping confidence intervals, academic mentoring was more highly related to performance than was youth or workplace mentoring. Both youth and academic mentoring were significantly associated with withdrawal behavior, whereas workplace mentoring was not. However, all three types of mentoring shared overlapping confidence intervals. Workplace and youth mentoring shared overlapping confidence intervals regarding helping others, but the effect size associated with workplace mentoring was significant whereas that associated with youth mentoring was not. With regard to attitudinal outcomes, all of the effect sizes were significant. However, the effect size associated with attitudes was stronger for academic than for youth mentoring. Regarding the health outcome of psychological stress & strain, the effect sizes were not significantly different from each other, but the workplace effect size was significant whereas the youth effect size was not. With regard to interpersonal relations, both were significant, but the effect size was stronger for workplace mentoring than for youth mentoring. Finally, with regard to motivational involvement, all three types of mentoring shared overlapping confidence intervals. However, whereas the effect sizes were significant for academic and workplace mentoring, the effect size was not statistically significant for youth mentoring. It is also noteworthy that when examining the specific types of mentoring, several medium effect sizes were detected. This pertains to the relationship between workplace mentoring and helping others ( r c = .26, p<.05) and to that between academic mentoring and school attitudes ( r c = .36, p<.05). Finally, for approximately half of the effect sizes list in Table 4 a significant Q statistic was found, indicating that additional moderators may exist.

Fixed-Effects Method Sub-Group Analyses

Four conclusions can be reached from our findings. First, we found that mentoring is significantly correlated in a favorable direction with a wide range of protégé outcomes. Second, although the overall effect sizes are small, mentoring appears to be more highly related to some protégé outcomes (e.g., school attitudes) than to others (e.g., psychological stress & strain). Third, there is evidence (albeit mixed) that there may be moderators of some mentoring-outcome relationships. Finally, there is tentative evidence of differences in the extent to which mentoring is associated with some outcomes across youth, academic, and workplace relationships.

Overall Findings

Our findings are generally consistent with previous reviews focusing on a specific type of mentoring (youth, academic, workplace). Both Allen et al. (2004) and Underhill (2006) found significant relationships between workplace mentoring and career attitudes, work attitudes, and some career outcomes. Reviews of youth ( DuBois et al., 2002 ) and academic ( Sambunjak et al., 2006 ) mentoring found an association between mentoring and both career and employment outcomes. There are also reviews linking youth ( DuBois et al., 2002 ), academic ( Dorsey & Baker, 2004 ; Sambunjak et al., 2006 ) and workplace ( Underhill, 2006 ) mentoring to psychological outcomes such as positive self-image, emotional adjustment, and psychological well-being, although similar to our findings, several of these reviews found small effect sizes. Finally, previous research on youth finds that being mentored is related to more positive social relationships, higher performance, and less problem behavior ( DuBois & Silverthorn, 2005 ).

Interestingly, our results suggest that mentoring is more strongly related to protégé attitudes than to behavior, health, and career outcomes. It may be that attitudes are more amenable to change than are outcomes that are more contextually-dependent or more influenced by stable person variables. For instance, an individual’s decision to engage in substance use may be strongly influenced by peer pressure, access to drugs, and parental role modeling, making it difficult for a mentoring relationship to have substantial impact. Likewise, research shows that career recognition and success is influenced by factors that may be outside one’s control (e.g., gender, race) and by factors not easily malleable (e.g., cognitive ability) ( Ng et al., 2005 ).

Differences by Type of Mentoring

Some interesting differences in effect sizes were found across the three types of mentoring included in the present review. The absolute value of the effect sizes associated with youth mentoring ranged from .03 to .14 while those associated with academic mentoring and with workplace mentoring ranged from .11 to .36 and .03 to .19, respectively. This pattern seems to suggest that generally speaking academic mentoring has stronger associations with outcomes than does youth mentoring and that workplace mentoring is somewhere in between. One possible explanation for these differences centers on the typical context under which these different types of mentoring occur. Specifically, youth who are mentored are often “at risk” for behavioral, social, or academic problems due to a poor family and/or socioeconomic situation. Thus, youth who are mentored commonly face numerous challenges (e.g., academic problems, parental conflict, unhealthy peer relationships) that may be difficult to overcome with mentoring alone ( DuBois et al., 2002 ). In fact, there is some evidence that youth mentoring leads to greater benefits when accompanied by other support services ( Kuperminc et al., 2005 ).

Given that youth may have many needs it may also be more difficult for mentors to offer focused and tailored guidance, especially when compared to the typical protégé within an academic or workplace setting. For example, academic mentoring relationships can generally be highly focused on a behavioral outcome such as performance because adolescents who have made it to a higher-level educational context have likely already surmounted or never faced some of the same obstacles. Thus, they are functioning at a higher level that does not require mentoring to be more diffuse and focused on multiple issues as may be the case in youth relationships. Another factor that could favor the effectiveness of academic mentoring is that this type of mentoring is often considered to be a core component of an institution’s mission ( Sambunjak et al., 2006 ). Moreover, mentors within the academic context may be better equipped to provide the functions associated with mentoring as it often part of their own job training. Often individuals who mentor youth or serve as informal mentors within the workplace setting do so on a volunteer basis with little or no training.

A final potential explanation for the pattern of effects centers on methodological differences in typical youth mentoring studies versus studies of other types of mentoring. Youth mentoring studies are more frequently based on a single mentoring relationship within a specific program and are often highly controlled in the form of random assignment of youth to receive or not receive a mentor. In contrast, in studies of academic or workplace mentoring the participant is often asked to simply report whether or not he or she has had a mentor. Youth mentoring studies are thus less likely to be influenced by self-selection biases (e.g., healthier individuals attract mentors) that have the potential to artificially inflate associations between mentoring and outcomes. In addition, intervention studies by their nature typically involve longitudinal associations between mentoring and outcomes at a later point in time, a factor that may further attenuate effect size estimates.

Implications for Multidisciplinary Research on Mentoring and Theory

The finding that mentoring is significantly correlated with a variety of positive protégé outcomes is consistent with conventional wisdom that close relationships are important for individuals across the lifespan ( Baumeister & Leary, 1995 ). As Allen and Eby (in press) note, individuals possess a universal and fundamental “need to belong” (p. 399). This need can be met through mentoring relationships and it may be an important driver of affective, cognitive, and behavioral outcomes for protégés. This desire for affiliation and acceptance from others can be met across the lifespan for those involved in youth, academic, and workplace mentoring relationships. This suggests that in order to acquire a broader understanding of the full range of mentoring benefits, researchers may profit from taking a more developmental lifespan approach to the study of mentoring. Such an approach will require greater cross-disciplinary dialogue.

The present meta-analytic review also identifies some outcomes of mentoring that deserve greater attention both across and within specific areas of mentoring scholarship. For example, it may be useful to further explore the link between mentoring and helping others since mentoring has been discussed as a form of prosocial behavior ( Allen, 2003 ). Examining helping behavior as a consequence of mentoring could lead to the further integration of existing research on altruism and organizational citizenship with mentoring. There also appear to be outcomes of mentoring that deserve greater attention within particular areas of mentoring scholarship. For example, career attitudes have been almost exclusively studied in workplace mentoring. However, a major goal of academic mentoring is career preparation. Therefore, it seems important to examine the relationship between mentoring received in college and subsequent career attitudes such as how satisfying one finds his or her career, expectations for career advancement, and perceived employment opportunities.

Applied Implications

There are several practical implications of our findings. Perhaps most importantly, we caution scholars, practitioners, and policy makers not to overestimate the potential effect of mentoring. Consistent with more focused reviews of the literature we found that the overall magnitude of association between mentoring and outcomes was small in magnitude. Moreover, due to the cross-sectional, non-experimental nature of many of the studies involved it is unknown whether significant correlations between mentoring and outcomes reflect a causal effect of mentoring. We are not suggesting that mentoring does not have value – the evidence presented here suggests that it may. However, we believe the results underscore the need to temper what are sometimes seemingly unrealistic expectations about what mentoring can offer to protégés, institutions, and society at large. We recommend that decision-makers think carefully when developing policies and programs about how to deal with pressing problems such as gang violence, teenage drug use, drop-out rates among diverse college students, and the loss of top talent in organizations. Mentoring may (or may not) be the best (or only) solution to a particular problem.

Our findings also provide guidance on the types of outcomes we might reasonably expect mentoring to influence. This could inform policy makers about the types of goals that formal mentoring programs might aim for with the greatest chance of success. In general, attitudes (e.g., work satisfaction, attitudes toward school, career expectations), interpersonal relations, and motivation/involvement may be the most easily influenced by mentoring, whereas health-related (e.g., substance use, psychological stress & strain) and career outcomes (e.g., promotions, salary) may be less influenced by mentoring. Looking at our findings by type of mentoring, we see that youth mentoring may be most likely to affect school attitudes and least likely to affect the performance, psychological stress & strain, or the motivation/involvement of protégés. In the academic arena mentoring may have the most utility in terms of improving performance and attitudes toward school and decreasing withdrawal behavior. Finally, in terms of workplace mentoring we find that larger gains may be likely in terms of enhancing helping behavior, situational satisfaction & attachment, and interpersonal relationships whereas smaller gains may be likely in terms of enhancing job performance and deterring withdrawal behavior.

Study Limitations

The current study has several limitations that should be noted. First, and most critically given the correlational nature of many of the studies included in this review, our findings do not provide unambiguous evidence that mentoring causes protégé outcomes. Rather, they provide encouragement to investigate this possibility within future research using more controlled designs (e.g., experimental) and investigating outcomes over time. The existing literature on mentoring literature has not widely adopted such designs. Second, we operationalized mentoring as the presence or absence of a mentor. However, there are other ways to examine mentoring such as the amount of mentoring received, relationship length, or relationship quality. The relationship between mentoring and protégé outcomes may differ based on how mentoring is operationalized. We encourage additional cross-disciplinary research that uses different conceptualizations of mentoring. Third, there were an insufficient number of studies to conduct sub-group analyses for all protégé outcomes or to compare all three types of mentoring. This leaves unexplored questions about the relative importance of mentoring across youth, academic, and workplace mentoring. Another limitation involves the trade-offs associated with using fixed-effects versus random-effects meta-analytic methods and the inconsistent findings these two methods provided with regard to our search for sub-group differences. Until additional studies become available for analysis, our sub-group analyses should be viewed tentatively. Likewise, the Fail-Safe N analysis calls into question the stability of several mentoring-outcome relationships. As such, the results reported in Tables 2 and ​ and3 3 should be considered in light of the number of unpublished studies estimated as necessary to obtain a p=.05.

In conclusion, our study represents the first attempt to quantitatively summarize the outcomes associated with mentoring across the three major areas of research: youth, academic, and workplace. The results suggest both similarities and differences in the benefits associated with different types of mentoring relationships, thus setting the stage for new areas of integration and future inquiry. The many positive benefits that our findings suggest could be associated with mentoring, albeit the small effect sizes, suggest that continued research that further helps us understand the dynamics and processes associated with mentoring across the lifespan is a worthwhile endeavor.

Acknowledgments

This research was supported in part by a grant from the National Institutes of Health (R01DA019460-02) awarded to Lillian T. Eby. The opinions expressed herein are those of the authors and not the granting agency. Great appreciation is expressed to Carrie Owen, Lauren Fields, Brian Roote, and Kanu Priya for their assistance on this project.

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Contributor Information

Lillian T. Eby, University of Georgia.

Tammy D. Allen, University of South Florida.

Sarah C. Evans, University of Georgia.

Thomas Ng, University of Hong Kong.

David DuBois, University of Illinois-Chicago.

(a list of studies included in the meta-analytic review is available from the first author)

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Mentoring in Nursing through Narrative Stories Across the World pp 441–448 Cite as

Mentoring in Research and Academia Is a Faculty Life Saver: Theoretical and Practical Evidence

• Elishba Khalil Akhtar 3 &
• Tazeen Saeed Ali 4  
• First Online: 16 July 2023

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Mentorship is defined as a collaborative relationship between individuals based on interdependence, cultivation, and trust. The benefits of using mentorship models, in the academic and clinical environments have been demonstrated multiple times within research literature. It has become increasingly vital for the students to receive guidance from a thesis mentor while undertaking their own thesis project. This is supported by literature review, which highlights the importance of mentorship programs in building research skills, encouraging critical thinking, and facilitating personal and professional growth. The World Health Organization has recognized the role of a mentor as a key component in training, sustaining, and maintaining the nursing profession. It has been observed that nursing students (mentees), especially those pursing graduate studies, are gravitating toward research under the guidance of nominated faculty members, who support them throughout their research journey. This chapter centers on the pivotal role of mentors in motivating students or mentees toward a positive change within their career, attitude, and relational aspect of academics.

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Akhtar, E.K., Ali, T.S. (2023). Mentoring in Research and Academia Is a Faculty Life Saver: Theoretical and Practical Evidence. In: Rollins Gantz, N., Hafsteinsdóttir, T.B. (eds) Mentoring in Nursing through Narrative Stories Across the World . Springer, Cham. https://doi.org/10.1007/978-3-031-25204-4_60

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Review article, the effect of mentoring on undergraduate mentors: a systematic review of the literature.

• 1 Department of Biology, University of Nebraska Omaha, Omaha, NE, United States
• 2 Offices of Undergraduate and Graduate Medical Education, University of Nebraska Medical Center, Omaha, NE, United States
• 3 STEM TRAIL Center, University of Nebraska Omaha, Omaha, NE, United States

Multiple meta-analyses and systematic reviews have been conducted to evaluate methodological rigor in research on the effect that mentoring has on the mentee. However, little reliable information exists regarding the effect of mentoring on the mentor. As such, we conducted a systematic review of the literature focused on such an effect (if any) within the fields of science, technology, engineering, and mathematics (STEM), aiming to better understand the quality of the research that has been conducted. We focused on undergraduate or post-secondary students as mentors for near-peers and/or youth. This review functions to identify commonalities and disparities of the mentoring program and research components and further promote methodological rigor on the subject by providing a more consistent description of the metrics utilized across studies. We analyzed articles from 2013 to 2020 to determine the features of undergraduate mentor programs and research, the methodological rigor of research applied, and compared them to prior research of this nature. In total, 80 eligible articles were identified through Cronbach’s UTOS framework and evaluated. Our key findings were that nearly all studies employed non-experimental designs, most with solely qualitative measurements and all lacked a full description of program components and/or experimental design, including theoretical framework. Overall, we identified the following best practice suggestions for future research on the effect of mentoring on mentors, specifically: the employment of longitudinal and exploratory mixed methods designs, utilizing sequential collection, and experimental descriptions nested within a theoretical framework.

Introduction

Programs focusing on undergraduates (UGs) providing mentoring are widespread within and outside of science, technology, engineering, and mathematics (STEM) disciplines. The effects of these programs are not beyond empirical analysis, with much of the existing research on mentoring focusing only on the impact of mentoring on mentees, objective data (e.g., exam scores, course grades, grade point average, etc.), or quantitative data ( Crisp and Cruz, 2009 ; Gershenfeld, 2014 ), which ultimately limits the scope of understanding and application. Our present study is a systematic review to determine the methodological rigor of research measuring outcomes for UG mentors (i.e., the individuals doing the mentoring, as opposed to those benefiting from the mentoring, as is commonly reported in the literature). We reviewed studies between 2013 and 2020, since 2014 (Gershenfeld) was the last publication on this topic and would not have included articles in press (i.e., during 2012 and published in 2013) at the time of its writing. In all, we identified 80 studies containing quantitative and/or qualitative insights from UG mentors.

Jacobi (1991) review of a decade (1980–1990) of mentoring research on mentor and mentee perspectives proposed a need for improved methodology and reasoned for the importance of situating mentoring programs and research within a theoretical base. Consequently, Jacobi (1991) put forward four major theoretical frameworks of mentoring programs: 1) involvement with learning, 2) academic and social integration, 3) social support, and 4) developmental support. Hannafin et al . (1997) indirectly extended and expounded upon this reasoning for use of the grounded theory design namely alignment of methods, theoretical or conceptual framework, and research are essential in understanding learning environments.

Nora and Crisp’s (2007) report on a survey of UG mentor perspectives and a corresponding literature review detailed the functional roles of mentors and prompted their assertion that mentoring programs and research continued to lack theoretical/conceptual bases. Nora and Crisp (2007) identified four major components that mentoring programs can utilize to provide a strong conceptual base namely 1) education/career goal establishment and evaluation, 2) emotional and psychological support, 3) academic content knowledge support, and 4) presence of a role model. Two years later, Crisp and Cruz, (2009) updated the review by Jacobi (1991) , outlining a continued lack of methodological rigor in a wider body of mentoring research between 1990 and 2007.

The last major review prior to this was conducted by Gershenfeld (2014) with the intention of extending the analysis of mentoring research to include published works between 2008 and 2012. Gershenfeld (2014) ultimately reported some improvement in the application of theoretical or conceptual frameworks but similarly outlined persistent methodological shortcomings. Of particular note, Gershenfeld (2014) identified some of what is termed “key mentoring program components” ( Supplementary Tables S1, 2 ) and innovatively applied the Levels of Evidence-Based Intervention Effectiveness (LEBIE; shown in Table 1 ; Jackson, 2009 ) scale to evaluate methodological rigor.

TABLE 1 . Levels of Evidence-Based Intervention Effectiveness scale (LEBIE). The LEBIE scale taken from Jackson (2009) and used by Gershenfeld (2014) . ED, Experimental design; QED, Quasi-experimental design; NED, Non-experimental design.

However, Gershenfeld (2014) identified a skew in article rankings by the LEBIE scale, assigning only 3’s, 4’s, and 5’s (which are inferior scores, as 5 = concerning). They attributed this skew to the scale’s rankings tending toward typical quantitative studies, in which the presence of equivalent controls and randomization is more common. In isolation, this issue would be significant, but Gershenfeld (2014) employed other forms of evaluation to ensure appropriate analysis of qualitative and mixed-method study designs, a strategy in which the present study adopts as well.

The aim of this study was to extend the analysis of research on the effect of mentoring on mentors, from the last review of such literature (i.e., the period covering 2013–2020). We aimed to address two key research questions:

1) Does the application of the LEBIE scale ( Jackson, 2009 ) to evaluate mentoring research that contains mentor perspectives published between 2013 and 2020 mirror that shared by Gershenfeld (2014) ? Or, did the field respond with more expansive mentoring evaluation practices after that publication?

2) Identify “key mentoring program components” ( Gershenfeld, 2014 ), theoretical or conceptual frameworks (if provided), methods, and general findings of the mentoring literature. We sought to determine what these components are, based upon the frameworks of Jacobi (1991) and Hannafin et al. (1997) , Nora and Crisp (2007), Crisp and Cruz (2009) , and Gershenfeld (2014) .

Ultimately, these results will allow for recommendations for future researchers to improve upon methodological rigor in research that studies the impact of mentoring on mentors.

Materials and Methods

The methods employed for this systematic review are consistent with the practices within the literature, namely of Cronbach and Shapiro (1982) and Moher et al. (2009) , using the following Cronbach’s units, treatments, outcomes, and study designs (UTOS) framework. Our population of interest ( Units ) is UG mentors within STEM and peripheral fields. We focused on the provision of mentoring by UGs ( Treatments ) as an intervention, including but not limited to mentoring within peer-mentoring, service-learning, course-related, internship, and research programs. The Outcomes we are interested in for eligibility are those reported openly by or requiring insights from UG mentors on what effect the experience had on them. Due to the exploratory nature of this study and the widely variable outcomes measured, we do not further constrict this parameter. However, we did also identify and report on other subjective components (e.g., demographics, compensation, support, frequency, etc.). As one of our major goals is to identify methods employed, all Study Designs are eligible for review, so long as outcomes are reported and are in line with the aforementioned parameter.

We completed a literature search within the Education Resources Information Center database (ERIC) and multiple databases within ESBCO (namely Academic Search Complete, Education Source, E-Journals, PsycARTICLES, PsycINFO, Psychology and Behavioral Sciences Collection, and Teacher Reference Center) using the respective search terms “mentor and undergraduate” in ERIC and “mentor and UG” in EBSCO. One set of search terms could not be used exclusively within both databases due to an issue with ERIC producing only two search results with the latter and EBSCO producing thousands of unrelated results with the former. Our other search criteria included scholarly articles, written in English, peer reviewed, and published between 2013 and 2020 (see Figure 1 for stepwise exclusion). We used a date range that included any articles published, while the Gershenfeld paper (i.e., the last most recent review) would have been under review (i.e., 2013) and through the final full year prior to submission (i.e., 2020). Therefore, this systematic review includes studies from 2013 to 2020, covering the entire ERIC database and multiple databases within EBSCO, and yielding 1,231 positive hits.

FIGURE 1 . Prisma flow diagram ( Moher et al., 2009 ) for record identification, inclusion, and exclusion. *Databases included within EBSCO search: Academic Search Complete, Education Source, E-Journals, PsycARTICLES, PsychINFO, Psychology and Behavioral Sciences Collection, and Teacher Reference Center.

After the removal of duplicates, the article titles and abstracts were screened for any indication of findings related to UG mentors (e.g., title and/or abstract explicitly contain the words undergraduate/UG mentors and suggest or explicitly state something about mentor perspectives/insight), which would fulfill our Units parameter. Those included through this initial screening were reviewed in full for eligibility if the focus was on the provision of mentoring by UGs, findings were reported, and insights from the mentors’ perspective were provided (i.e., explicit statements and data were provided to demonstrate each), therefore fulfilling our Outcomes and Treatment parameters. Articles or programs pertaining to service-learning were included only if the service-learning involved provision of mentoring by UGs, and any articles or programs concerning traditional pre-service teaching internship programs (e.g., co-teaching within a classroom setting under the supervision of a certified teacher) were excluded, as such positions do not revolve around the adoption of a mentor role. While mentors may certainly serve as teachers and teachers may certainly serve as mentors, they are generally observed and/or measured as separate roles albeit closely related ( Crisp and Cruz, 2009 ; Gershenfeld, 2014 ; Jacobi, 1991 ; Nelson et al., 2017; 2017; Nora and Crisp, 2007 ), prompting our decision to exclude pre-service programs in order to maintain focus on mentoring in alignment with our Treatment parameter.

Throughout each step of the review process, two authors (ASL and KLN) independently read and evaluated relevant articles/sections (e.g., abstract vs. methods vs. whole document), addressing any discrepancies prior to moving on. We routinely compared independent running documents containing all positive hits and subsequent inclusions of articles/extracted data (i.e., independent versions of Figure 1 , Table 1 , and Supplementary Tables ) in a stepwise manner, while the third author (CEC) addressed any discrepancies not clearly resolved by the other two (e.g., whether certain language indicated a program to be a pre-service program). Nearly all inclusion criteria and data that we collected were concerned with the presence or absence of some attribute or the reporting of what is explicitly stated or not stated by article authors and were based on priorly established frameworks as discussed in our research questions. For this reason, many inconsistencies between reviewers could be attributed to one of the authors missing a qualifying article or not identifying data. However, some discrepancies did arise from unclear language or subjective interpretation (e.g., analytic logic, sequencing, and data priority in studies utilizing mixed methods design). Inconsistencies of the former type were resolved by comparing data sets and identifying where the criteria or data were located within articles/sections, while discrepancies of the latter type were resolved through discussion with the third author.

In total, there were 1,231 positive hits through the database query after duplicate removal. Of these, n = 80 met all of our inclusion criteria and were analyzed by the following evaluative tools. We used the LEBIE scale ( Jackson, 2009 ) to examine methodological rigor ( Table 1 ) in terms of study design (e.g., presence of equivalent vs. non-equivalent vs. no control group) and evidence of effectiveness (e.g., evidence that intervention results in some positive change over time or is better than or comparable to a control/placebo). To examine program and research functionality and qualities, we used ( Nora and Crisp, 2007) conceptualization of core functional roles (e.g., assist with a course, provide peer-mentoring, service-learning, etc.) and ( Gershenfeld, 2014) key-mentoring program and research components (namely mentor and mentee demographics, compensation, frequency of mentoring, support, N = number of mentors, quantitative vs. qualitative vs. mixed methods, how data are collected, and major findings). In line with prior researchers from Jacobi (1991) and Hannafin et al. (1997) to Nora and Crisp (2007) and Gershenfeld, (2014) , we also identified theoretical/conceptual frameworks (if stated by authors).

Finally, for relevant studies, we examined characteristics deemed essential within the literature to mixed methods designs ( Supplementary Tables S3 ), including an explicit statement that mixed methods research is being utilized, rationale for using mixed methods research, integration of quantitative and qualitative data (merging, connecting, or building), analytic logic (independent or dependent), sequencing/timing (concurrent or sequential), and/or priority (quantitative, qualitative, or both; Creswell, 2013; Creswell and Plano Clark, 2017 ; Harrison et al., 2020 ; O’Cathain et al., 2008 ; plano Clark and Ivankova, 2016 ). We took the former three from eligible studies (i.e., stated or not and what was stated) but interpreted the latter three for all but one. Ultimately, our results will consist of LEBIE scale ratings, compiled qualitative data on program and research qualities, and reporting of relative proportions of qualities where possible. Of note, where we discuss proportions/percentages, the sample size (n) may not equal the total number of eligible studies ( n = 80) due to some qualities not being reported or present in certain studies (e.g., mixed methods design), and percentages may add up to be greater than 100% due to certain studies reporting multiple elements within a given quality (e.g., different types of compensation given to different participants).

Consistent with prior research, we have included many components of the articles we reviewed and the mentoring programs they analyzed (contained within the following table and supplemental materials). It is and always was our intention to compile this large amount of data in order to provide easy access to overview these studies for other mentoring researchers (we have grouped similar data together for this reason). However, our primary goal is to identify trends within mentoring programs and research approaches in addition to analyzing methodological rigor in studies on the subject in order to provide suggestions for improvement of future research. To this end, our results and discussion will be focused on our research questions to determine rigor (i.e., Table 1 , Supplementary Tables S1, 2 ) and methodology (i.e., Supplementary Tables S2, 3 ).

Rigor in the Experimental Design for Mentoring Articles

Mirroring Gershenfeld (2014) review, we analyzed rigor by the LEBIE scale and components deemed essential to mentoring and mentoring research within the literature ( Jacobi, 1991 ; Hannafin et al., 1997 ; Nora and Crisp, 2007 ; Crisp and Cruz, 2009 ). Our rankings by use of the LEBIE scale ( Table 1 ) were consistent with Gershenfeld (2014) review (only Level 5s, 4s, and 3s are given) but with considerable regression onto Level 4 (Gershenfeld assigned eleven Level 5s, four Level 4s, and three Level 3s). Of note, we only ranked one article as efficacious (Level 3) and one other as concerning (Level 5). For all remaining articles (78 of n = 80) included in this review, we assigned the rank of emerging (Level 4), with 11 containing some form of pre- and post-intervention measurement.

While reviewing articles for theoretical/conceptual frameworks ( Supplementary Tables S2 ), we recorded any that were explicitly stated (61.25%, n = 49) and also identified those that relate to at least one of the four major theoretical frameworks of mentoring programs put forward by Jacobi (1991; 45%, n = 36). For program functionality ( Supplementary Tables S1 ), our concern was with the type of mentoring (i.e., peer, near-peer, and youth), whether the authors considered other core functions (i.e., internship and service-learning), and which of ( Nora and Crisp, 2007) four major components were present. We found that 65% ( n = 52) of articles contained programs for peer mentoring, 22.5% ( n = 18) for near-peer mentoring, 32.5% ( n = 26) for youth mentoring, 22.5% ( n = 18) for service-learning, and 2.5% ( n = 2) for internships. Concerning ( Nora and Crisp, 2007) four major components, our analysis found 45% ( n = 36) of programs to be solely or primarily focused on academic content and knowledge support, 8.75% ( n = 7) to include discussion and focus on all four components, and the remainder to be focused on other single components or combinations of at least two of the four.

Type of Method for Data Collection Utilized

The majority (70%, n = 56; methods; Supplementary Tables S2 ) of articles we reviewed employed qualitative methodologies, and a small minority employed quantitative methodologies (6.25%, n = 5) or were systematic reviews (3.75%, n = 3). Our inspection shows that the number of mentors or sample sizes (N; Supplementary Tables S2 ) within the included studies is considerably variable, ranging from 1 to 1,972. Additionally, some articles did not report N at all or reported it vaguely (e.g., greater than 150). We found that a large portion of studies collected data (data collection; Supplementary Tables S2 ) through self-report surveys (38.75%, n = 31), and of these many were Likert scale–based (18.75%, n = 15). A total of twelve articles (15%) used priorly developed tools for quantitative measurements, and the remaining data collection methods were made up by spread and/or variable combinations of interviews, document analysis, focus groups, observation, demographic information, general feedback, or commentary, and questionnaires. While 9 studies (methods; Supplementary Tables S2 ) did explicitly state the use of the mixed methods design, we analyzed another 7 that contained both quantitative and qualitative data collection as employing the mixed methods design (20%, n = 16 employed mixed methods design).

Key Qualities of Mixed Methods Research in Relevant Articles

All of the articles we identified as utilizing mixed methods designs explicitly stated the use of qualitative and quantitative measures, and just over half of these (56.25%, n = 9; Supplementary Tables S3 ) also explicitly stated the utilization of mixed methods design. Less than half of these (37.5%, n = 6) articles state a mode of integration (all but one report integration by triangulation) and seven (43.75%) studies provide no evidence of combining quantitative and qualitative data sets. The outlier ( Hastings and Sunderman, 2019 ) reports integration by using qualitative data to build on and support quantitative data and is the only article to include explicit details on analytic logic (dependent), sequencing/timing (quantitative prior to qualitative), and priority (quantitative, the only article with this priority). For the remaining articles, we interpreted that 68.75% ( n = 11) had even priority between quantitative and qualitative data, 25% ( n = 4) prioritized qualitative data, and all but one study (87.5%, n = 14) had independent analytic logic and concurrent sequencing/timing [ McIntosh (2019) ; could not be interpreted due to a lack of methodological description]. Of the studies that did not explicitly state integration (62.5%, n = 10), one provided some discussion of using qualitative and quantitative data to build on each other ( Pica and Fripp, 2020 ), and two discussed looking for common patterns in each ( Köse and Johnson, 2016 ; Bonner et al., 2019 ).

Present State of Research According to This Review

Our LEBIE scale rankings are consistent with but do not directly mirror that shared by Gershenfeld (2014) , suggesting that mentoring research between 2013 and 2020 has, in general, responded with at least some more expansive mentoring evaluation practices after its publication. However, the proportion of articles explicitly stating the adoption of a theoretical or conceptual framework in our systematic review is smaller than previously reported, and the most common and predominating functions from Nora and Crisp’s (2007) four major components are largely used in academic content and knowledge support ( Gershenfeld, 2014 ). Considering best practice in mentoring programs and research ( Jacobi, 1991 ; Hannafin et al., 1997 ; Nora and Crisp, 2007 ; Crisp and Cruz, 2009 ), we reason that a decrease in theoretical bases and lack of change in functional grounding suggests a general decrease in methodological rigor that is not measured by the LEBIE scale.

Our analysis of article methodology is meant to augment these findings, as LEBIE scale rankings and functional component identification do not evaluate the full spectrum of methodological designs within the field. The vast majority of studies we have identified through this systematic review employ qualitative-only designs over singular and relatively short time periods, and most utilize self-report surveys (Likert scale or otherwise) developed for the sole purpose of evaluating the program of interest. Additionally, we examined that qualitative or quantitative measurements generally were not taken pre-/mid- and post-intervention.

In programs that have employed mixed methods research, we found that evidence of quantitative and qualitative data integration was lacking and that methodological description was often limited or not present. Curiously, we identified the article by ( Hastings and Sunderman (2019 ) as providing the most detailed methodological description that employed an exploratory mixed methods design but used quantitative measurement for exploration and qualitative data for support. This is in opposition to recommendations in the literature for exploratory mixed methods studies ( Creswell and Plano Clark, 2017 ; Harrison et al., 2020 ), in which qualitative then quantitative data are sequentially collected, and the latter depends on the former. Our systematic review suggests that there remains a lack of valid and reliable tools for quantitative measurement of the effect of mentoring on UG mentors and leading exploration with qualitative measurements is more likely to provide progress toward the development of such tools ( Creswell and Plano Clark, 2017 ; Harrison et al., 2020 ).

Ultimately, our analyses of UG mentor program components and function ( Table 1 and Supplementary Tables S1 ) demonstrate even more variability than priorly identified ( Gershenfeld, 2014 ). Alongside the invariability of LEBIE scale (Table 2) rankings presently and previously ( Gershenfeld, 2014 ), this reinforces the need for methodological rigor and evaluation appropriate to such a complex subject. Accordingly, our suggestions for future researchers on the effect of mentoring on UG mentors are that there is a need for studies of the longitudinal design ( Plano Clark et al., 2015 ), of an exploratory nature ( Gershenfeld, 2014 ), utilizing a sequential collection of qualitative and then quantitative data ( Creswell and Plano Clark, 2017 ; Harrison et al., 2020 ). We recognize that research completed to analyze mentoring programs is often constricted by the variable nature of its components and participant characteristics. None of these suggestions should necessitate the application of all others, as the employment of even a single one would be beneficial to methodological rigor (e.g., well-established qualitative exploration to understand where quantitative measurements are most beneficial and appropriate).

Limitations

The limitations of this review include our bias in focusing solely on the effect of mentoring on mentors at the omission of discussion on the effect on mentees. Conjecture back and forth on the latter effect has occurred and is ongoing at length elsewhere, and we, therefore, chose not to include it in this article. Another limitation of note would be the scope of databases queried for this systematic review, namely the Education Resources Information Center database (ERIC) and multiple databases within the ESBCO. These databases represent a sizeable group, with a focus that should include a representative sample of research relevant to this review. However, it is possible that articles meeting our inclusion criteria were missed if their publishing journals were not contained within the aforementioned databases.

Suggestions for Future Researchers

Collecting data over longer and multiple periods of time should provide more information on whether and/or what long term effects of mentoring can realistically be expected ( Plano Clark et al., 2015 ; Nelson and Cutucache, 2017 ), while more rigorous quantitative data collection and analysis would provide studies with more generalizability ( Kruger, 2003 ) and increased objectivity ( Linn et al., 2015 ; Owen, 2017 ). Moreover, by employing exploratory and longitudinal mixed-method designs, methodological rigor can be improved ( Creswell and Plano Clark, 2017 ; Harrison et al., 2020 ) and progress can be made toward the development of tools for valid and reliable quantitative measurement, hopefully creating a cycle of reciprocity.

We further assert that it is vital for studies on this topic to provide descriptions and explicit statements relating to their methodology, program, and participants. Many of the studies we identified in this systematic review did not share important details, requiring interpretation and a lot of time to properly evaluate and understand them. Providing information explicitly not only improves the ease of access for future researchers but is also valuable to methodological rigor by encouraging the adoption of theoretical/conceptual frameworks ( Jacobi, 1991 ; Gershenfeld, 2014 ) and fleshing out mentor and program functionality ( Nora and Crisp, 2007 ; Gershenfeld, 2014 ).

Author Contributions

AL, KN, and CC conceived the study idea. AL and KN collated and analyzed the articles for inclusion. AL, KN, and CC wrote and edited several drafts of the manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

Thanks to the Department of Biology, the Honors Program, and the University of Nebraska at Omaha for providing the resources for this project.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2021.731657/full#supplementary-material

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Keywords: stem education, UG mentoring, rigor, methods, systematic review

Citation: Leavitt AS, Nelson KL and Cutucache CE (2022) The Effect of Mentoring on Undergraduate Mentors: A Systematic Review of the Literature. Front. Educ. 6:731657. doi: 10.3389/feduc.2021.731657

Received: 27 June 2021; Accepted: 07 December 2021; Published: 31 January 2022.

Reviewed by:

Copyright © 2022 Leavitt, Nelson and Cutucache. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Christine E. Cutucache, [email protected]

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Master of Science Degree in Epidemiology and Clinical Research

MS students in the Graduate Epidemiology and Clinical Research program have two mentors. Upon entering the program, each student is assigned a methodology mentor, usually a core faculty member. In addition, the student will be asked to identify a research mentor. Research mentors are typically members of the Stanford University professoriate but need not be designated as faculty in EPH. For physicians, this mentor is usually from the discipline of the student’s clinical affiliation. For example, physicians who maintain an affiliation with another university can often arrange to include a research mentor from the other university if the student’s research takes place, in part, at that university. If the research mentor is from EPH, then the same faculty member may serve as both methodology and research mentor with permission of the program director. 

The methodology mentor serves as the student’s Academic Advisor and is responsible for advising the selection of courses, approving a thesis research topic, monitoring the student’s progress through the program, and helping with other program-related issues that may arise. Suppose a student’s thesis research requires additional expertise not covered by the methodology or research mentors (e.g., outcomes research or advanced statistical methods). In that case, a third mentor may be appointed with the approval of the methodology mentor.

The completion of a master's thesis is an essential component of the MS degree, allowing students a chance to integrate epidemiologic principles learned in courses and to demonstrate:

• Familiarity with epidemiologic terms, reasoning, and issues.
• Ability to communicate scientific reasoning and argue analytically.
• Awareness of technical, methodological, and other issues relevant to traditional and clinical epidemiologic research.
• Comprehension of statistical techniques, their proper use, and limitations.
• Knowledge in a substantive area.

The thesis is usually 30 to 60 pages long, double-spaced, and includes tables, figures, and references. Each thesis must include a summary abstract of approximately 400 to 800 words. The thesis can take one of the four following forms:

• Original analysis of data, whether collected primarily for the thesis or as secondary data analysis. This thesis form is most commonly selected by students.
• A comprehensive literature review with a meta-analysis of data or a critical reanalysis of data.
• Evaluation of a methodologic problem using real or hypothetical data.
• A comprehensive literature review with a grant proposal (NIH-style format) for a new study to bridge a gap in the existing knowledge. The proposal should highlight methodologic principles.

The quality of the thesis should be such that it can be converted into a manuscript for publication or a credible research grant application, and students are strongly encouraged to do so. Students are required to present their research findings during a session of EPI 236: Epidemiology Research Seminar.

Thesis Committee

Each student’s Master’s Thesis Committee is composed of at least two faculty members, an epidemiology core reader, and a co-reader. The epidemiology core reader, who is typically the student’s methodology mentor, serves as the principal thesis advisor. The co-reader is typically the research mentor. The epidemiology core reader is ordinarily a member of the Stanford Academic Council and should be listed as the instructor for at least 9 of the required 12 Master’s thesis (research) units (EPI 399).  

Registration for Master’s thesis units must be approved by the core reader. If the student’s thesis research requires expertise beyond that covered by the mentoring team described above, a third faculty mentor may be appointed as a thesis reader. This appointment must be requested by the student and approved by the core reader. Primary supervision during thesis research and writing is shared by the core reader and the co-reader.

Completion of the Master’s thesis involves registration for at least 12 units of Master’s thesis research over a period of two or more quarters. During the first quarter of research, a proposal for the thesis must be submitted to thesis readers when the project is early in its conceptual stages. The purpose is for the student to obtain guidance from the Thesis Committee about specific aims, study design features,  and analytic methods before commencing the project. The Master’s Thesis Committee will notify the student of its decision within two weeks of receipt of the proposal. Rejected proposals can be resubmitted before the end of the quarter.

Registration for the second quarter of Master’s thesis units can occur only after successfully completing the first quarter thesis requirements. In the quarter the student expects to graduate, the  Master’s thesis should be completed and submitted to the readers, allowing sufficient time for readers’  comments and for revisions that might be required. A student should ordinarily expect readers’  comments within two weeks of submission. The final version should be submitted at least two weeks before the end of the quarter. A suggested format for the thesis is available from the department's Educational Program Manager. An electronic copy of the approved thesis, with three original signature sheets, should be sent to the Binding and Finishing department at least 72 hours before the deadline. Send the PDF, including the signature page, to the Educational Program Manager.

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VP&S Students Honored for Research Achievements

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Twelve students, pictured here with medical school leaders, won awards at VP&S Student Research Day on April 3. Front row, from left: Amy Shteyman, Madison Heath, Aleksandra Recupero, Paul Lewis, Meghana Giri, William Britton, Guillermo Almodovar Cruz. Back row, from left: Kavya Rajesh, Alice Vinogradsky, Anil Lalwani, Katrina Armstrong, Monica Lypson, Prashanth Kumar, Frederick Lang. Not pictured: Damian Teasley. Photo by Diane Bondareff.

Students at Columbia’s Vagelos College of Physicians and Surgeons were honored recently for their achievements in research.

At the 2024 Student Research Day on April 3, 74 students presented their research to colleagues, fellow students, faculty, and leadership. The presentations included research posters with explanations of the students’ methodologies, results, and inspiration. Twelve projects won awards in four categories: MD/PhD, Research Year, Scholarly Project, and Summer Research. See more photos from the event .

“The quality of research showcased by VP&S medical school students mirrors their excellence and our faculty’s dedication to mentoring,” says Anil Lalwani, MD, associate dean for student research and professor of otolaryngology/head & neck surgery.

Student Research Day also recognized a Scholarly Projects Faculty Mentor of the Year, an award celebrating mentors who have shaped the scholarly project experience. The 2024 winner is Syed Ali Husain, MD, assistant professor of medicine.

Syed Ali Husain, center, pictured with Anil Lalwani, left, and Utpal Pajvani, won the 2024 Scholarly Projects Faculty Mentor of the Year. Photo by Diane Bondareff.

VP&S Student Research Day Winners

• 1st Place: Aleksandra Recupero, “Optimization of an Automated Behavioral Platform to Assess the Developmental Timing of Behavioral Maturation” (mentor: Franck Polleux, PhD) 

Research Year

• 1st Place: William Britton, “Interrogating a Partial Epithelial-to-Mesenchymal Transition Program in 3D Patient Derived Organoids of Head and Neck Squamous Cell Carcinoma” (mentors: Anuraag Parikh, MD; Hiroshi Nakagawa, MD)
• 2nd Place: Paul Lewis, “Adolescents Caring for Community by Promoting Literacy on Insurance, Stroke, Health Education, Emergencies, and Dementia (ACComPLISHED): A Community Health Worker (CHW) Program” (mentors: Olajide Williams, MD, MS; James Noble, MD, MS)
• 3rd Place (tie) : Kavya Rajesh, “Development and In Vivo Characterization of Biohybrid Conduits for Growing Heart Valve Replacements” (mentor: David Kalfa, MD, PhD) and Damian Teasley, “Local Delivery of Topotecan Induces Immunogenic Cell Death and Associated Myeloid Response in GBM” (mentors: Peter Canoll, MD, PhD; Jeffrey Bruce, MD)

Scholarly Project

• 1st Place: Frederick Lang, “SGLT2 Inhibitors for Transthyretin Amyloid Cardiomyopathy: Short-Term Analyses of Efficacy and Safety” (mentor: Mathew Maurer, MD)
• 2nd Place: Alice Vinogradsky, “Long-Term Outcomes of Heart Transplantation in Adults with Congenital Heart Disease: The Impact of Single-Ventricle Versus Biventricular Physiology” (mentor: Koji Takeda, MD, PhD)
• 3rd Place: Madison Heath, “Gut Colonization with Multidrug Resistant Organisms in the Intensive Care Unit: A Systematic Review and Meta-Analysis” (mentor: Daniel Freedberg, MD, MS)

Summer Research

• 1st Place: Amy Shteyman, “Characterizing the Effects of Focused Ultrasound Therapy in Healthy and Alzheimer’s Disease Neurons” (mentor: Elisa Konofagou, PhD)
• 2nd Place: Guillermo Almodovar-Cruz, “Impact of heart transplant allocation policy change on outcomes of extracorporeal life support for cardiogenic shock with acute decompensated heart failure versus acute myocardial infarction” (mentor: Koji Takeda, MD, PhD)
• 3rd Place (tie) : Prashanth Kumar, “Association of MGMT Promotor Methylation with Survival in Low-grade and Anaplastic Gliomas After Alkylating Chemotherapy” (mentor: Connor Kinslow, MD) and Meghana Giri, “Engaging Youth in Pollution and Lung Health Monitoring in Washington Heights, New York City: A Pilot Study” (mentor: Stephanie Lovinsky-Desir, MD)

In a symposium on March 22, 14 members of the VP&S Class of 2024 in the MD-MS Program in Biomedical Sciences presented their master’s thesis research to faculty and students. The MD-MS Program in Biomedical Sciences offers students the opportunity to nurture a research interest and engage in a year of original research in a field relevant to medicine.

Students in the MD-MS Program in Biomedical Sciences participated in a March 22 symposium. Back row, from left: Anil Lalwani, Halil Beqaj, Nicholas Waring, Ogoegbunam Okolo, Alexander Northrop, Colin Sperring, Adithya Kannan, Michael Denham, Benjamin Redenti, Christian Pearsall, Monica Lypson. Front row, from left: Sharon Feng, Celine Schreidah, Cole Chokran, Averill Clapp. Not pictured: Zainab Aziz. Photo by Brandon Vallejo.

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The Two Men Who Wanted to Categorize ‘Every Living Thing’ on Earth

Jason Roberts tells the story of the scholars who tried to taxonomize the world.

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By Deborah Blum

Deborah Blum, the director of the Knight Science Journalism Program at M.I.T., is the author of “The Poison Squad: One Chemist’s Single-Minded Crusade for Food Safety at the Turn of the Twentieth Century.”

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EVERY LIVING THING: The Great and Deadly Race to Know All Life , by Jason Roberts

A professor asks a student to go on a plant-collecting trip, a perilous journey from Sweden to Suriname in 1754. The devoted student agrees, which means months tossed about on a wooden ship while chased by a simmering fever. When the student returns, he still shows hints of delirium, declaring that one of his specimens can produce a harvest of pearls, refusing to turn over any of his treasures to his mentor. What’s a plant-obsessed professor to do?

For Carl Linnaeus, this was easily answered. He went to Daniel Rolander’s home and, finding him away, smashed a window and broke in. Sadly, he found no pearl-bearing oyster plant or any other notable vegetation; merely one small herb which people in Suriname used to treat diarrhea. Linnaeus took it anyway. He then dismissed the young collector entirely, denying him compensation and pointedly naming a minuscule beetle “Aphanus rolandi.” (“Aphanus” means obscure, by the way.)

If this sketch of Linnaeus causes you to view the man as ruthless, a little unhinged and a lot meanspirited, well, that’s the point here. Jason Roberts, the author of “Every Living Thing,” is not a fan of the founding father of taxonomy, whom he rather hilariously describes as “a Swedish doctor with a diploma-mill medical degree and a flair for self-promotion.” But the snark is not merely entertainment — the portrait is central to the main thesis of Roberts’s engaging and thought-provoking book, one focused on the theatrical politics and often deeply troubling science that shape our definitions of life on Earth.

Roberts’s exploration centers on the competing work of Linnaeus and another scientific pioneer, the French mathematician and naturalist Georges-Louis Leclerc, Comte de Buffon. Of the two, Linnaeus is far better known today. Of course, Roberts notes, the Frenchman did not pursue fame as ardently as did his Swedish rival. Linnaeus cultivated admiration to a near-religious degree; he liked to describe even obscure students like Rolander as “apostles.” Buffon, in his time even more famous as a brilliant mathematician, scholar and theorist, preferred debate over adulation, dismissing public praise as “a vain and deceitful phantom.”

Their different approaches to stardom may partly explain why we remember one better than we do the other. But perhaps their most important difference — one that forms the central question of Roberts’s book — can be found in their sharply opposing ideas on how to best impose order on the planet’s tangle of species.

Linnaeus is justly given credit for applying logic and order to science, standardizing the names, definitions and classifications of research. But his directives were based on an often uncharitable and deeply biased worldview. He saw species, including humans, as needing to be ranked according to European values. Thus, Linnaeus is also credited with establishing racial categories for people.

He placed white Europeans firmly at the top. Homo sapiens Europaeus, as he called it, was blond, blue-eyed, “gentle, acute, inventive.” By contrast, Homo sapiens Afer was dark and, in Linnaeus’s definition, “slow, sly and careless”; Homo sapiens Americanus was red-skinned and short-tempered.

Buffon, far more generous by nature, rejected this racial hierarchy. “The dissimilarities are merely external,” he wrote in 1758, “the alterations of nature but superficial.” Living things were adaptable, he insisted, shaped by the environment. Charles Darwin, who pioneered the theory of evolution, would later call Buffon’s ideas, posed more than a century before the 1859 publication of “On the Origin of Species,” “laughably like my own.”

Roberts stands openly on the side of Buffon, rather than his “profoundly prejudiced” rival. He’s frustrated that human society and its scientific enterprise ignored the better ideas — and the better man. And he’s equally frustrated that after all this time we’ve yet to fully acknowledge Buffon’s contributions to our understanding. As time has proved him right, certainly on issues of race and evolution, Roberts asks, why are Linnaeus and his worldviews still so much better known — and better accepted by far too many?

The book traces some reasons — the anti-aristocratic fervor of the French Revolution in suppressing Buffon’s scholarship; the European colonialists who firmly elevated Linnaeus’s more convenient worldview. It wasn’t until the 20th century that scientists and historians began rediscovering the importance of the French scientist’s ideas. And that, Roberts believes, has been our loss in countless ways.

More than 250 years ago, Buffon proposed that we exist in a world full of ever-changing possibility, a place where our similarities matter as much as our differences. Perhaps it’s not too late, this book suggests, to be our better selves and yet hear him out.

EVERY LIVING THING : The Great and Deadly Race to Know All Life | By Jason Roberts | Random House | 422 pp. | $35

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