• Users Online: 259
  • Print this page
  • Email this page

Table of Contents
Year : 2022  |  Volume : 5  |  Issue : 1  |  Page : 4-12

Lack of correlation between pre-veterinary school experience hours and DVM course performance: Pros and cons of veterinary work experience as a prerequisite for admission to veterinary school

Department of Clinical Sciences, North Carolina State University College of Veterinary Medicine, Raleigh, North Carolina, United States

Date of Submission24-Aug-2021
Date of Acceptance03-Jan-2022
Date of Web Publication23-May-2022

Correspondence Address:
Dr. M Katie Sheats
Department of Clinical Sciences, North Carolina State University College of Veterinary Medicine, 1060 William Moore Dr., Raleigh, NC 27607
United States
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/EHP.EHP_25_21

Rights and Permissions

In this article, we explore the issue of prerequisite veterinary experience hours as a requirement for veterinary school applications. Our interest in this topic began with an investigation into the correlation between species-specific animal experience hours reported in Veterinary Medical College Application Service (VMCAS) applications and third-year grades in companion animal, equine, and ruminant medicine courses for 288 veterinary students. We hypothesized that species-specific experience hours prior to veterinary school would correlate with grades in species-specific courses, particularly in equine and ruminant-focused courses. Using an isometric-log regression analysis, we found no significant association between final course grades and total, or species-specific, veterinary experience hours reported in VMCAS applications. We propose that these data support the assertion that students with wide ranges of pre-veterinary animal experience hours can be successful in third-year Doctor of Veterinary Medicine (DVM) species-specific medicine and surgery courses. With this finding in mind, we discuss the potential benefits and drawbacks of veterinary work experience as a prerequisite for DVM program admission. Although additional studies are needed, we suggest that DVM program admissions criteria should be carefully reexamined with particular consideration for unintentional barriers to equity and inclusivity within the veterinary profession.

Keywords: Diversity, healthcare admissions criteria, veterinary education veterinary prerequisite, veterinary student performance

How to cite this article:
Kortum A, Huckel J, Robertson J, Sheats M K. Lack of correlation between pre-veterinary school experience hours and DVM course performance: Pros and cons of veterinary work experience as a prerequisite for admission to veterinary school. Educ Health Prof 2022;5:4-12

How to cite this URL:
Kortum A, Huckel J, Robertson J, Sheats M K. Lack of correlation between pre-veterinary school experience hours and DVM course performance: Pros and cons of veterinary work experience as a prerequisite for admission to veterinary school. Educ Health Prof [serial online] 2022 [cited 2022 Sep 28];5:4-12. Available from: https://www.ehpjournal.com/text.asp?2022/5/1/4/345787

  Introduction Top

Acceptance into veterinary colleges in the United States is an extremely competitive process. Among the many attributes that make a successful applicant are high-grade point averages, extracurricular activities, and demonstration of interest and commitment to the field through prior veterinary-related experience. Applicants to veterinary universities are encouraged to gain experience in various veterinary fields and report them when applying for enrollment through the Veterinary Medical College Application Service (VMCAS). Although VMCAS does not have a minimum required number of veterinarian-supervised experience hours, individual schools do have hour requirements and/or recommendations, with minimums ranging from 1 to 500 h. A number of schools stress the importance of in-depth, quality experience that spans a breadth of veterinary species.

Numerous studies have sought to determine which preadmissions qualifications predict academic success for students entering healthcare professions including nursing, pharmacy, and human and veterinary medicine.[1],[2],[3] These qualifications are often divided into cognitive and noncognitive categories. Consistent with results in other fields, several veterinary education studies have shown that cognitive admissions criteria, such as pre-veterinary Grade Point Average (GPA) and standardized test scores, are positively correlated with academic performance in the first, or first and second year, of veterinary school.[4],[5],[6],[7] The value of noncognitive admissions criteria (e.g., reference letters and work experience) for predicting Doctor of Veterinary Medicine (DVM) student outcomes is less well studied in the veterinary education literature. This is in contrast to other health professions, where noncognitive admissions criteria, such as interviews and personality tests, are the subject of ongoing research[3],[8] and discussion.[9]

One veterinary study on preadmissions work experience found that a student’s clinical experience with particular types of animals––companion animals, horses, food animals––tended to predict their career preferences.[10] However, to the best of our knowledge, no studies have examined the relationship between pre-veterinary school animal experience hours and veterinary school academic performance. Although some studies suggest that noncognitive admissions factors, such as work experience, are not predictive of academic performance,[6],[11],[12] we hypothesized that species-specific experience hours prior to veterinary school would correlate with grades in species-specific courses, particularly in equine and ruminant focused courses. Our rationale for this hypothesis was that students with practical veterinary experience with individual species would have a greater baseline knowledge than students without previous experience, and that experience would benefit students in terms of course performance. We also reasoned that students who displayed more pre-veterinary interest in certain species might be more motivated to study for courses focused on that species, thereby performing better in the course. Positive correlation between student work experience or student interest/motivation and academic achievement does have support in the literature. Previous work experience was more associated with GPA than Graduate Management Admission Test (GMAT) score or undergraduate grade point average in a study of MBA students.[13] Previous work experience was also predictive of academic performance in an accounting degree program.[14] Finally, students with a year of supervised work experience had better academic performance in the final year of an undergraduate accounting degree program.[15] We know from educational psychology literature that student interest is connected with behaviors that support learning, including attention, concentration, and interest in reading reference materials.[16] Numerous studies, including several meta-analyses, also provide evidence that student motivation is significantly related to academic performance.[17],[18],[19],[20] The purpose of this study was to determine whether species-specific animal experience hours reported in VMCAS applications correlated with DVM student final grades in species-specific third-year veterinary medicine courses.

  Materials and Methods Top

Collection and analysis of student data was approved by the North Carolina State University Institutional Review Board (IRB# 22240).


Grades from four third-year veterinary courses were obtained for three DVM classes (Class of 2018, 2019, and 2020). Identifying information was removed prior to data analysis. The four semester-long courses were Companion Animal Medicine and Surgery I (VMC 951), Companion Animal Medicine and Surgery II (VMC 961), Equine Medicine and Surgery (VMC 952), and Ruminant Medicine and Surgery (VMC 962). Available letter grades were converted into a numerical value using the minimum number of the range each letter represented: A+ = 97, A = 94, A- = 91, B+ = 88, B = 85, B- = 82, C+ = 79, C = 76, C- = 73, D+ = 70, D = 67, and D– = 64. The total numbers of grades for each course were as follows: 297 grades for Companion Animal Medicine and Surgery I, 297 grades for Equine Medicine and Surgery, 296 grades for Companion Animal Medicine and Surgery II, and 295 grades for Ruminant Medicine and Surgery.

Animal experience

VMCAS applications for the 2014, 2015, and 2016 application cycles were reviewed and identifying information was removed. These application cycles corresponded to the accepted veterinary classes of 2018, 2019, and 2020. The “Veterinary Experience” portion of the Supporting Information in the applications was extracted. Experiences reported in this section of the application included veterinary clinical, agribusiness, or health science, and were limited to veterinary research or animal experience that is supervised by a veterinarian. For each veterinary experience, students indicated experience with a minimum of one and a maximum of five species (small animal, food animal, equine, zoo, and exotic). If more than one species was listed for a veterinary experience, VMCAS equally divided the total hours among all species listed for that experience (i.e., prorated the experience hours).

Subjects and exclusion criteria

VMCAS application data and medicine course grades were collected from a total of 310 students and of these, 22 were excluded on the following basis: 8 students did not have corresponding VMCAS application data, 13 students did not have any medicine course grades reported, and 1 student did not have grades reported for VMC 961 and 962.

Statistical analysis

Course grades and animal experience hours were analyzed for normality using Shapiro–Wilk, by descriptive statistics including median and lower and upper quartiles, and compared among classes using Kruskal–Wallis and, if applicable, Dunn’s multiple comparison test. Animal experience hours were normalized by summing each student’s total animal experience hours and dividing each category of animal experience by the total in order to create a proportion of student time. These values were used for graphical displays in the ternary diagrams where each vertex represents 100% of time being in that category of experience. The opposite edge is then no time spent on that category of experience. For regression analysis, the proportion of time in each category was then divided by the proportion of small animal experience (arbitrary reference number) and log transformed. This new logratio was then centered at zero by subtracting the mean of the logratio transformed values and subsequently multiplied by an orthonormal basis vector.

Multiple linear models were fit with the isometric log-regression transformed composition of small animal, food animal, and equine experience as well as the total hours in those three categories as predictors. Models used the data from all three veterinary cohorts. Due to some students having no experience outside of small animals, Wilcoxon rank sum tests were also used to determine if there was any difference in the grades between students with only small animal experience and other students. Results were obtained using R version 4.0.2[21] with packages ggtern[22] and compositions.[23] A value of P < 0.05 was considered significant.

  Results Top

Descriptive statistics

As third year students, the Class of 2018 had a median age of 25 years (IQR = 24–27) and were 77% female. Reported ethnicities reported were 78% white, 13% underrepresented minority (LatinX/Hispanic; African American/Black, Asian, Native Alaskan, Native American) and 9% multi-racial/multi-ethnic. The Class of 2019 had a median age of 25 years (IQR = 24–28) and were 75% female. Reported ethnicities were 69% white, 24% underrepresented minority (LatinX/Hispanic; African American/Black, Asian, Native Alaskan, Native American) and 7% multi-racial/multi-ethnic. The Class of 2020 had a median age of 25 years (IQR = 24–27) and were 76% female. Reported ethnicities were 78% white, 17% underrepresented minority (LatinX/Hispanic; African American/Black, Asian, Native Alaskan, Native American) and 5% multi-racial/multi-ethnic. [Table 1]. Although there was a significant difference in mean ranks for Companion Animal Medicine and Surgery II (VMC 961) between Class of 2018 vs. 2019 (86.06 ± 5.19 vs. 88.24 ± 4.79, P = 0.0076), and Class of 2018 vs. 2020 (86.06 ± 5.19 vs. 88.99 ± 4.88, P < 0.0001), this difference was not considered consequential in terms of downstream analysis. The numbers of total and species-specific pre-veterinary school experience hours were not significantly different across the three DVM classes [Table 1].
Table 1: Descriptive statistics for grades and hours of pre-veterinary experience across three veterinary school classes

Click here to view

Lack of relationship between course grades and preadmission experience

Using an isometric-log regression analysis, there was no significant association between final course grades and total, or species-specific, veterinary experience hours reported in VMCAS applications. In addition, there was no statistically significant difference found between students with only small animal experience and those with other experience and the grade they earned in any course.

Students performing in the lower quartile for grades have a variety of pre-veterinary experiences

Ternary diagrams were constructed to show the ratio of species-specific hours accumulated by each applicant and those that earned a C+ or below (red dots) in each course. Each point in the diagram represents a student and each vertex of the figure represents 100% experience with that species [Figure 1]. A dot along the edge of the diagram but equidistant between two vertexes represents a student with equal experience with two species. A dot toward the very center of the diagram represents a student with equal experience with three species.
Figure 1: Composition plots of hours of preadmission veterinary experience with C+ or below highlighted in red. (A) Companion Animal Medicine and Surgery I (VMC 951). (B) Equine Medicine and Surgery (VMC 952). (C) Companion Animal Medicine and Surgery II (VMC 961). (D) Ruminant Medicine and Surgery (VMC 962)

Click here to view

The diagrams show that students with grades in the lower quartile (red dots) have a range of pre-veterinary animal experience hours in different species. Similarly, students with grades in the middle and upper quartiles (non-red dots) also have a range of reported pre-veterinary animal experience hours. Consistent with our logistic regression, these diagrams show that final grades in species-specific medicine and surgery courses are not associated with the composition of a student’s prior animal work experience hours.

  Discussion Top

The purpose of this study was to determine whether species-specific animal experience hours reported by students in VMCAS applications correlated with DVM student final grades in species-specific third-year veterinary medicine courses. We were interested in this topic for several reasons. First, we have anecdotal reports that students perceive species-specific animal experience prior to veterinary school as an advantage in terms of third-year course performance and we wanted to determine whether these perceptions held merit. Second, our institution is currently revising our knowledge-based curriculum to a competency-based veterinary education (CBVE) curriculum. Prior to this change, we wanted to understand any association between this admissions criterion and student performance. Third, requirements for pre-veterinary school experience hours could represent an unintentional barrier to creating a diverse and inclusive veterinary class. Therefore, we wanted to examine this prerequisite in order to inform discussion on the potential advantages and disadvantages of experience hours as an admissions requirement.

Our analyses found no significant association between accumulated hours of experience, total or species-specific, and third-year medicine course outcomes as measured by grades. These data disprove our initial hypothesis and show that students from all levels of previous veterinary work experience can succeed in high-level graduate veterinary course material.

There are several possible explanations for our findings. First, previous studies have shown that measures of cognitive performance prior to veterinary school are the best predictors of cognitive performance in veterinary school[4],[5],[7],[11],[24] and supervised veterinary work experience is considered noncognitive criteria. Therefore, some might argue that lack of correlation between these data was a forgone conclusion. However, we reasoned that veterinary animal knowledge gained from pre-veterinary work experience could potentially benefit veterinary students in species-specific courses. For example, from previous equine work experience, students could have enhanced working knowledge of equine anatomy, performance disciplines, gaits, behavior, and common and uncommon presenting complaints that could potentially help with recognizing and localizing presenting problems and generating differential lists. Previous work experience has been shown to benefit student academic performance in MBA graduate programs,[13] but did not correlate with grade performance in health professions programs such as nursing,[25] pharmacy[26] or medicine.[27] The reasons for these differences among professional degree programs is not clear, but in veterinary medicine it could be that pre-veterinary work experience is more observational or technical than knowledge-based, thereby providing little advantage to students in knowledge-based medicine and surgery courses. A similar explanation was proposed in a study that failed to show a correlation between medical students’ self-reported clinical experience (as a yes/no variable) and five outcomes, including cumulative medical school GPA, US Medical Licensing Examination (USMLE) Step 1 and 2 scores, and evaluation scores measuring intern expertise and professionalism.[27] Like us, Artino et al. were somewhat surprised by their study results. They suggest that their findings undermine a long-standing assumption within medical admissions committees that a candidate with previous clinical experience is more desirable than one without that experience.

Medical-school admissions committees report using applicants’ previous clinical work experience as an indicator of student motivation toward, and interest in, medicine.[28] Similarly, we reasoned that DVM applicants with species-specific pre-veterinary work experience might have a strong interest in that species, which could translate into a higher motivation for learning and improved academic performance. However, we did not survey students regarding their reasons for obtaining pre-veterinary work experience with specific species. One survey study of medical students showed that students obtain work experience more as an aid to get into medical school, and less as a reflection of their personal interest.[29] To the best of our knowledge, no studies have examined the reasons pre-veterinary students do or do not obtain work experience with different animal species.

Although our data fail to link previous veterinary work experience with third-year DVM course grades, there are other potential benefits of work experience as a prerequisite for veterinary school admission. For young adults choosing a career, relevant work experience is an invaluable means to determine whether the profession is a good fit. Students who decide not to pursue veterinary school because of their work experience are making an informed choice, and have saved both time and money. For students who decide to apply to veterinary school, veterinary work experience has given them the opportunity to form meaningful relationships with practicing DVMs, to experience the positive and negative aspects of the job, and to have a better understanding of the industry. In fact, one study that analyzed VMCAS personal statements found that 38% of students reported prior veterinary experience as the reason they chose to pursue veterinary medicine.[30]

For veterinary schools, knowledge of an applicant’s veterinary and animal-specific experience could help target admissions to meet societal needs.[31] This is particularly relevant for fields of veterinary medicine that are currently struggling to attract and retain new graduates, such as food animal medicine. A recent report from the University of Minnesota College of Veterinary Medicine showed that lived experiences such as growing up on a farm, or participation in 4H or Future Farmers of America, as well as previous shadowing of a large animal veterinarian, were characteristics of students who chose, and stayed in, a food animal focus track.[31] Recently, there has been similar interest in understanding medical students’ previous work experience and career interests, specifically for general practice, which is a field that is experiencing a workforce crisis. Three separate studies have shown that personal experience in general practice and greater contact with GPs is associated with more positive student attitudes toward general practice.[29],[32],[33]

Previous veterinary work experience may benefit students entering CBVE training programs. CBVE, introduced by the AAVMC in 2018, is an outcomes-based, learner-centered education and assessment framework designed to ensure veterinary program graduates are “practice-ready” in nine domains of competence.[34] Students with previous veterinary work experience often have advanced animal handling and technical skills before they are even admitted to veterinary school. Comfort with basic technical skills such as physical examination and diagnostic sample collection will likely help students advance along the CBVE Milestones continuum more rapidly.[35]

Students with previous veterinary experience may also have an advantage in terms of cognitive load. Cognitive load theory addresses the differences in the amount of effort a student has to exert in order to learn new skills and/or content.[36] This theory explains that students attending a learning task must divide their working memory between three types of cognitive load: intrinsic, extraneous, and germane. The specific task or concept determines the level of intrinsic cognitive load; the way the material is delivered, including learning activities and environment, determines the level of extraneous cognitive load; and the effort of moving new knowledge into long-term memory is germane cognitive load.[37] Learners with prior animal experience or education are often already familiar with pertinent concepts, jargon, lay terms, husbandry techniques, handling, physical examination findings, and methods of sample collection. This familiarity decreases extraneous cognitive load, allowing these students to use more of their working memory for learning the new content or skills, and adding that information to previously constructed schema, which increases their level of expertise.[38] Cognitive load is a very important concept for veterinary education because veterinary training programs are under tremendous pressure to deliver a vast amount of student learning in a relatively short amount of time. As veterinary medicine shifts to CBVE, new studies will be needed to better understand the links between applicant qualities and DVM student experience in terms of cognitive load and competency development.

Although there are clear arguments for the potential benefits of veterinary work experience as a prerequisite for veterinary school admission, there are also potential drawbacks to be considered. For one, there are other types of previous work experience besides “veterinary experience” that could benefit student success in a CBVE training program. Specifically, CBVE consists not only of technical skills, but also communication, professional skills, and collaboration. Communication was identified as the most important nontechnical competency in a systematic review of veterinary literature and includes the ability to listen and communicate professionally while adapting one’s communication style appropriately to colleagues and clients.[34],[39] Collaboration includes being able to function as a leader and team member and show inclusivity and cultural competence.[34] All of these attributes are characteristics of successful members of the service and hospitality industry. Students who focus solely on gaining veterinary experience prior to veterinary school may miss opportunities to begin building nontechnical competencies that would be beneficial in a CBVE curriculum. Interestingly, one study found a group of students who were not accepted into veterinary school scored higher on nontechnical competencies compared to admitted students, suggesting the selection process may be screening out individuals with desirable attributes.[40]

Another potential drawback of placing too much value on the requirement for veterinary work hours prior to veterinary school is the potential for this experience to narrow the career focus for some students. This idea is supported by data from one institution that uses a tracking system, which allows students in the later stages of the curriculum to take courses that are more specific to their intended career paths.[41] Authors found that the most significant factor influencing a student’s initial choice of tracking was whether their background experience matched the experiences necessary for their chosen track.[41] In the same study, 27% of students changed tracks after the second year of the curriculum. The top reasons students gave for the change were that they were exposed to an area that interested them (25%), the track would allow a better quality of life (19%), and the track had more employment opportunities (13%).[41] A more comprehensive study representing 32 North American veterinary schools similarly found that 20% of students changed their career focus after entering veterinary school, and the primary reason for the change was expanded interests due to exposure in veterinary school.[42] Root Kustritz et al.[31] also reported that later in the curriculum, 40.8% of veterinary student respondents agreed/strongly agreed that “coursework” influenced their track choice. Therefore, although pre-veterinary work experience can help to predict a student’s career,[31],[41] it is not a guarantee of student choice.

Finally, we think it is important to consider how required experience hours could affect the educational institution and the future workforce of veterinary medicine. In the three DVM classes we analyzed for this study, the total student reported animal experience was 2040 (1756, 2325) h (mean, 95% confidence interval [CI]). One way to look at this number is that it gives prospective applicants a goal to strive for if they are serious about getting into veterinary school. However, another perspective is that it might disproportionately disadvantage certain demographics of students, as these experiences may be harder to come by for applicants from lower socioeconomic classes, older individuals with personal responsibilities such as families, and students from particular geographic regions. For example, pre-veterinary work experience often comes in the form of volunteer or low-wage positions, which discriminates against students from lower socioeconomic backgrounds. Older or second-career applicants may not be at a disadvantage financially, but rather by time constraints. Individuals who choose to pursue veterinary medicine as a second career, or who have families, may not be able to dedicate hundreds of hours to gaining experience. Lastly, students from rural communities may only have access to a few veterinary professionals within a large geographic area, which could limit paid working opportunities. Similar concerns have been raised regarding equity of access to general practice work experience for pre-medical students, with one study showing that “…being in the ‘other’ ethnic group, from lower socioeconomic groups, and attending a state school decreased the odds of having a general practice experience” among medical school applicants.[33] Factors that contribute to a lack of exposure to the career have been linked with the lack of racial and ethnic diversity in the veterinary profession.[43],[44],[45],[46]

The negative effect of admissions criteria on diversity in medical education has been studied and reviewed extensively.[47],[48],[49] This inequity is of great concern to the medical community, as a physician workforce that reflects the patient population has a clear benefit to advancement of patient care, science, and health equity.[50] The same is undoubtedly true for veterinary medicine. In human medicine, the current recommendation is for admissions policies to be holistic, flexible, and responsive to evidence of both effectiveness and unintended consequences[48],[51],[52] (Ballejos et al., 2015). Medical schools are actively altering access criteria (i.e., prerequisites) to enhance diversity and inclusivity,[53] and at the same time, they are developing initiatives to actively support and recruit a more diverse pool of prepared applicants.[54],[55] There is also evidence that listening to the perspectives of minority and nontraditional students can lead to new strategies to address issues of equity in the admissions process.[56] In one study, Ball et al.[55] conducted in-depth interviews with medical students from nontraditional backgrounds in order to explore their process of “getting ready” for medical school. This study found that this group of students identified role models as critical for gaining confidence to apply to medical school, and lack of resources, such as networks, qualifications, money, and prestige, as diminishing their confidence to apply. Given these results, it is plausible that the excessive numbers of veterinary experience work hours reported for successful DVM applicants creates an intimidation factor for students with limited resources, even when they meet the minimum criteria.

Our investigation had several limitations. Although there was a wide range of student-reported animal experience hours, the lack of range in final course grades could have contributed to our inability to discern the benefit of previous species-specific work experience, as the majority of final course grades skewed to the right. Also, we might have missed the academic performance benefit of species-specific work experience by looking at third-year course performance, as training in first- and second-year courses may have effectively “evened the playing field” for students from diverse experience backgrounds by the time they enter third year. It is also important to point out that the VMCAS work experience hours are self-reported by students and are not independently verified, which may lead to lack of accuracy in the data. However, as mentioned by Artino et al. in a similar study on self-reported clinical experience in medical school applicants, if admissions committees rely on self-reported data in the admissions process, they should seek evidence for the value and validity of that data.[27] Another limitation of our study was that the experience hours obtained from VMCAS were prorated when students selected more than one species for a single veterinary experience. However, we sought to address this limitation by normalizing the data, and conducting ternary analysis, which looked at student experience as a composition of three different species. Finally, we acknowledge that our evaluation of grades in four third-year courses is certainly not a comprehensive reflection of the success of veterinary students. Our results do not take into account the nonacademic skills or qualities that may have been obtained during those experiences and how they influence clinical performance. Although future studies are needed to investigate associations between veterinary school application prerequisites and student success in CBVE training programs, the complexity of evaluating student competencies makes this difficult. For example, in 2008, many schools were still experimenting with assessment methods, generally choosing to employ more than one.[57] This issue is further complicated by the variation in terminology and language pertaining to competencies in the literature.[34],[58] Moving forward, standardization and consistency in CBVE will undoubtedly enable more robust studies that will help to address these gaps in knowledge.

In summary, this investigation showed no significant relationship between total, or species-specific, hours of veterinary experience gained prior to veterinary school admission and final grades in four different third-year medicine courses. These findings support the assertion that students with a wide range of pre-veterinary animal experience hours can be successful in knowledge-based third-year DVM species-specific medicine and surgery courses. Future studies will be needed to investigate how previous work experience, both veterinary and non-veterinary, impacts student experience and achievement as more veterinary schools transition to CBVE. Finally, requirements for veterinary school application, including prerequisite experience hours, should be evaluated to determine whether they disproportionately disadvantage minority and/or nontraditional students from entry into the veterinary profession.


We would like to thank Ms. Sandy Sferrazza for providing and de-identifying the raw grade data and admissions information.

Financial support and sponsorship


Conflicts 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.

  References Top

Wilcox RE, Lawson KA. Predicting performance in health professions education programs from admissions information: Comparisons of other health professions with pharmacy. Curr Pharm Teach Learn 2018;10:529-41.  Back to cited text no. 1
Zamanzadeh V, Ghahramanian A, Valizadeh L, Bagheriyeh F, Lynagh M. A scoping review of admission criteria and selection methods in nursing education. BMC Nurs 2020;19:121.  Back to cited text no. 2
Ferguson E, James D, Madeley L. Factors associated with success in medical school: Systematic review of the literature. BMJ 2002;324:952-7.  Back to cited text no. 3
Hudson NPH, Rhind SM, Mellanby RJ, Giannopoulos GM, Dalziel L, Shaw DJ. Success at veterinary school: Evaluating the influence of intake variables on year-1 examination performance. J Vet Med Educ 2020;47:218-29.  Back to cited text no. 4
Rush BR, Sanderson MW, Elmore RG. Pre-matriculation indicators of academic difficulty during veterinary school. J Vet Med Educ 2005;32:517-22.  Back to cited text no. 5
Zachary JF, Schaeffer DJ. Correlation between preveterinary admissions variables and academic success in core courses during the first two years of the veterinary curriculum. J Vet Med Educ 1994;21:56-61.   Back to cited text no. 6
Powers DE. Validity of graduate record examinations (GRE) general test scores for admissions to colleges of veterinary medicine. J Appl Psychol 2004;89:208-19.  Back to cited text no. 7
Yingling S, Park YS, Curry RH, Monson V, Girotti J. Beyond cognitive measures: Empirical evidence supporting holistic medical school admissions practices and professional identity formation. MedEdPublish 2018;7.  Back to cited text no. 8
Witzburg RA, Sondheimer HM. Holistic review: Shaping the medical profession one applicant at a time. N Engl J Med 2013;368:1565-7.  Back to cited text no. 9
Serpell JA. Factors influencing veterinary students’ career choices and attitudes to animals. JVME 2005;32:491-3.  Back to cited text no. 10
Fuentealba C, Hecker KG, Nelson PD, Tegzes JH, Waldhalm SJ. Relationships between admissions requirements and pre-clinical and clinical performance in a distributed veterinary curriculum. J Vet Med Educ 2011;38:52-9.   Back to cited text no. 11
Sulaiman A, Mohezar S. Student success factors: Identifying key predictors. J Educ Bus 2010;81:328-33.  Back to cited text no. 12
Adams AJ, Hancock T. Work experience as a predictor of MBA performance. Coll Stud J 2000;34:211+.  Back to cited text no. 13
Koh MY, Koh HC. The determinants of performance in an accountancy degree programme. Account Educ 1999;8:13-29.   Back to cited text no. 14
Gracia L, Jenkins E. A quantitative exploration of student performance on an undergraduate accounting programme of study. Account Educ 2010;12:15-32.  Back to cited text no. 15
Silvia PJ. Interest: The curious emotion. Curr Dir Psychol Sci 2008;17:57-60.  Back to cited text no. 16
Steinmayr R, Weidinger AF, Schwinger M, Spinath B. The importance of students’ motivation for their academic achievement: Replicating and extending previous findings. Front Psychol 2019;10:1730.  Back to cited text no. 17
Linnenbrink-Garcia L, Wormington SV, Snyder KE, Riggsbee J, Perez T, Ben-Eliyahu A, et al. Multiple pathways to success: An examination of integrative motivational profiles among upper elementary and college students. J Educ Psychol 2018;110:1026-48.  Back to cited text no. 18
Robbins SB, Lauver K, Le H, Davis D, Langley R, Carlstrom A. Do psychosocial and study skill factors predict college outcomes? A meta-analysis. Psychol Bull 2004;130:261-88.   Back to cited text no. 19
Huang C. Self-concept and academic achievement: A meta-analysis of longitudinal relations. J Sch Psychol 2011;49:505-28.  Back to cited text no. 20
R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing; 2020.  Back to cited text no. 21
Hamilton NE, Ferry M. “ggtern: Ternary diagrams using ggplot2.” J Stat Soft Code Snip 2018;87:1-17.  Back to cited text no. 22
van den Boogaart KG, Tolosana-Delgado R, Bren M. Compositions: Compositional Data Analysis. R package version 2.0-0. 2020. Available from: https://CRAN.Rproject.org/package=compositions. [Last accessed on 2022 Feb 17].  Back to cited text no. 23
Holladay SD, Gogal RM, Moore PC, Tuckfield RC, Burgess BA, Brown SA. Predictive value of veterinary student application data for class rank at end of year 1. Vet Sci 2020;7:1-13.  Back to cited text no. 24
Yousafzai II, Jamil B. Relationship between admission criteria and academic performance: A correlational study in nursing students. Pakistan J Med Sci 2019;35:858.  Back to cited text no. 25
Mar E, Barnett MJ, T-L Tang T, Sasaki-Hill D, Kuperberg JR, Knapp K. Impact of previous pharmacy work experience on pharmacy school academic performance. Am J Pharm Educ 2010;74:42.  Back to cited text no. 26
Artino Jr AR, Gilliland WR, Waechter DM, Cruess D, Calloway M, Durning SJ. Does self‐reported clinical experience predict performance in medical school and internship? Med Educ 2012;46:172-8.  Back to cited text no. 27
Monroe A, Quinn E, Samuelson W, Dunleavy DM, Dowd KW. An overview of the medical school admission process and use of applicant data in decision making. Acad Med 2013;88:672-81.  Back to cited text no. 28
Park J, Philipp R, Hughes A. Do we value work experience before medical school? Br J Gen Pract 2005;55:242-3.  Back to cited text no. 29
Amass SF, Davis KS, Salisbury SK, Weisman JL. Impact of gender and race-ethnicity on reasons for pursuing a career in veterinary medicine and career aspirations. J Am Vet Med Assoc 2011;238:1435-40.  Back to cited text no. 30
Kustritz MV, Malone E, Rendahl A. Identification of student lifestyle characteristics associated with training choices to drive targeted admissions in veterinary medicine. Educ Health Prof 2020;3:70-6.  Back to cited text no. 31
  [Full text]  
Henderson E, Berlin A, Fuller J. Attitude of medical students towards general practice and general practitioners. Br J Gen Pract 2002;52:359-63.  Back to cited text no. 32
Agravat P, Ahmed T, Goudie E, Islam S, McKechnie DG, Abdirahman HM, et al. Medical applicant general practice experience and career aspirations: A questionnaire study. BJGP Open 2021;5:BJGPO.2021.0023.  Back to cited text no. 33
Matthew SM, Bok HGJ, Chaney KP, Read EK, Hodgson JL, Rush BR, et al. Collaborative development of a shared framework for competency-based veterinary education. J Vet Med Educ 2020;47:578-93.  Back to cited text no. 34
Salisbury SK, Chaney KP, Ilkiw JE, Read EK, Rush BR, Bok HGJ, et al. Competency-Based Veterinary Education: Part 3 – Milestones. Washington, DC: Association of American Veterinary Medical Colleges; 2019.  Back to cited text no. 35
Sweller J. Cognitive load during problem solving: Effects on learning. Cogn Sci 1988;12:257-85.   Back to cited text no. 36
Hawthorne BS, Vella-Brodrick DA, Hattie J. Well-being as a cognitive load reducing agent: A review of the literature. Front Educ 2019;4:121.  Back to cited text no. 37
Khalil MK, Mansour MM, Wilhite DR. Evaluation of cognitive loads imposed by traditional paper-based and innovative computer-based instructional strategies. J Vet Med Educ 2010;37:353-7.  Back to cited text no. 38
Cake MA, Bell MA, Williams JC, Brown FJL, Dozier M, Rhind SM, et al. Which professional (non-technical) competencies are most important to the success of graduate veterinarians ? A Best Evidence Medical Education (BEME) systematic review: BEME Guide No. 38. Med Educ 2016;38:550-63.  Back to cited text no. 39
Ilgen DR, Lloyd JW, Morgeson FP, Johnson MD, Meyer CJ, Marrinan M. Personal characteristics, knowledge of the veterinary profession, and influences on career choice among students in the veterinary school applicant pool. J Am Vet Med Assoc 2003;223:1587-94.  Back to cited text no. 40
Chigerwe M, Boudreaux KA, Ilkiw JE. Factors affecting track selection by veterinary professional students admitted to the school of veterinary medicine at the university of California, Davis. J Vet Med Educ 2010;37:154-8.  Back to cited text no. 41
Andrus DM, Gwinner KP, Prince JB. Job satisfaction, changes in occupational area, and commitment to a career in food supply veterinary medicine. JAVMA 2006;228:1884-93.  Back to cited text no. 42
Elmore RG. Reasons for the lack of racial diversity in veterinary medicine. J Vet Med Educ 2004;31:414-6.  Back to cited text no. 43
Brown S. The under-representation of African Americans in animal welfare fields in the United States. Antrozoos 2008;18:98-121.  Back to cited text no. 44
Greenhill L, Elmore R, Stewart S, Carmichael KP, Blackwell MJ. Fifty years in the life of veterinary students. J Vet Med Educ 2015;42:480-8.  Back to cited text no. 45
Morse EM. Minority student perceptions of the veterinary profession: Factors influencing choices of health careers. Doctoral dissertation, Cleveland State University. 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=csu1232135567. [Last accessed on 2022 Feb 17].  Back to cited text no. 46
Wouters A. Getting to know our non-traditional and rejected medical school applicants. Perspect Med Educ 2020;9:132-4.  Back to cited text no. 47
Cleland JA, Patterson F, Hanson MD. Thinking of selection and widening access as complex and wicked problems. Med Educ 2018;52:1228-39.  Back to cited text no. 48
Patterson F, Roberts C, Hanson MD, Hampe W, Eva K, Ponnamperuma G, et al. 2018 Ottawa Consensus Statement: Selection and recruitment to the healthcare professions. Med Teach 2018;40:1091-101.  Back to cited text no. 49
Conrad S, Addams A, Young G. Holistic review in medical school admissions and selection: A strategic, mission-driven response to shifting societal needs. Acad Med 2016;91:1472-4.  Back to cited text no. 50
Felix H, Laird J, Ennulat C, Donkers K, Garrubba C, Hawkins S, et al. Holistic admissions process: An initiative to support diversity in medical education. J Physician Assist Educ 2012;23:21-7.  Back to cited text no. 51
Ballejos MP, Rhyne RL, Parkes J. Increasing the relative weight of noncognitive admission criteria improves underrepresented minority admission rates to medical school. Teach Learn Med 2015;27:155-62.  Back to cited text no. 52
Muller D, Kase N. Challenging traditional premedical requirements as predictors of success in medical school: The Mount Sinai School of Medicine Humanities and Medicine Program. Acad Med 2010;85:1378-83.  Back to cited text no. 53
Figueroa. The significance of recruiting underrepresented minorities in medicine: An examination of the need for effective approaches used in admissions by higher education institutions. Med Educ Online 2014;19:1.  Back to cited text no. 54
Ball R, Alexander K, Cleland J. “The biggest barrier was my own self”: The role of social comparison in non-traditional students’ journey to medicine. Perspect Med Educ 2020;9:147-56.  Back to cited text no. 55
Hadinger MA. Underrepresented minorities in medical school admissions: A qualitative study. Teach Learn Med 2017;29: 31-41.  Back to cited text no. 56
Hardie EM. Current methods in use for assessing clinical competencies: What works? J Vet Med Educ 2008;35:359-68.  Back to cited text no. 57
Englander R, Cameron T, Ballard AJ, Dodge J, Bull J, Aschenbrener CA. Toward a common taxonomy of competency domains for the health professions and competencies for physicians. Acad Med 2013;88:1088-94.  Back to cited text no. 58


  [Figure 1]

  [Table 1]


    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

  In this article
Materials and Me...
Article Figures
Article Tables

 Article Access Statistics
    PDF Downloaded90    
    Comments [Add]    

Recommend this journal