Abstract
Although there is extensive research literature on clinical skill competencies and the use of competency-based frameworks for clinical research, the appropriate methods to assess these competencies are not as well understood. Our goal in this systematic literature review is to identify, compare, and critique assessments of clinical research competencies. Articles were included in this review if they examined clinical investigators or clinical investigators in training, focused on research-based skills, and included some form of assessment of research-based competencies. A total of 76 articles were identified as part of the initial search; 16 met the criteria for inclusion. Two types of assessments of clinical research competence were identified: subjective self-assessments (n = 13) and objective tests (n = 6). These assessments covered a wide range of competencies, but there were no competency domains common to all. Most assessments had limited validation. Training was consistently associated with self-assessed competence but had little relationship to objective measures of competence. In contrast, experience was consistently associated with objectively assessed competence but not with self-assessed competence. These findings have important implications for those interested in assessing medical education programs. We describe a recommended standard for validity for assessments used for the purposes of summative program assessment.
Medical care is advanced by scientific innovations made by clinical researchers. However, one of the main barriers to the conduct of clinical research is the lack of qualified, well-trained clinical investigators (Berglund & Tarantal, 2009; Dickler, Korn, & Gabbe, 2006; Meador, 2015). Ensuring that clinical researchers are competent is important because low-quality clinical research has a negative effect on public health and exacts a toll on investigators’ time and limited institutional resources (Altman, 1994; Glasziou & Chalmers, 2018). Leading clinical research investigators and administrators continue to emphasize the critical importance that clinical and translational research training plays in meeting the future needs of the scientific enterprise (Collins, Wilder, & Zerhouni, 2014). Since its inception, the Clinical and Translational Science Awards (CTSA) program, which is funded through the National Institutes of Health and administered by the National Center for Advancing Translational Science (2019), has sought to train the next generation of researchers. As a result, the number of institutions offering fellowship training, master’s degrees, and PhD degrees in clinical research has grown significantly.
To ensure that basic concepts and skills related to the conduct of clinical and translational research are taught, many programs are adopting competency-based models of education (Dilmore, Moore, & Bjork, 2013; Robinson, Moore, McTigue, Rubio, & Kapoor, 2015). Work has been ongoing to develop core competencies for clinical research professionals and research investigators. The careful identification and measurement of key skills that enable clinical scientists to conduct quality clinical research continues to be an important component of this broader trend. Research productivity metrics, particularly bibliometric outcomes, provide useful information about the effectiveness of research training programs (Knapke et al., 2013). However, studies of research publications suggest that such metrics are not sufficient for measuring the efficacy of research training programs. First, standard productivity metrics may present a misleading picture of the quality of research training programs, due to the potential for inflation of publication counts via self-citation (Bartneck & Kokkelmans, 2011). Moreover, clinical research papers themselves are often compromised by methodological errors (Ioannidis et al., 2014), a lack of replicability and reproducibility (Begley & Ellis, 2012; Drucker, 2016; Peers, Ceuppens, & Harbron, 2012; Prinz, Schlange, & Asadullah, 2011), and statistical errors (Garcia-Berthou & Alcaraz, 2004; Strasak, Zaman, Marinell, Pfeiffer, & Ulmer, 2007). While standard bibliometric outcomes do include proxy measures of research quality, they are limited in their ability to reflect measures of underlying research skill possessed by the investigators who succeed in publishing their research. Therefore, a critical need exists to move beyond indirect measures of research ability and develop more competency-based approaches that enable more direct measurement of clinical and translational research skills.
Competence in clinical and translational research means possessing the requisite knowledge, skills, abilities, and behaviors (competencies) needed to perform specific, defined research tasks. However, it has been difficult to define which knowledge, skills, and abilities translational researchers need to demonstrate (Rubio et al., 2010), and the assessment of competence in translational research has likewise made slow progress (Robinson et al., 2015). This is partly because competence requires good metacognition: the awareness of one’s own performance and judgment, and limits thereof (Kruger & Dunning, 1999). However, the ability to accurately self-assess is often imperfect, particularly for those with less training or experience.
Several frameworks have been developed for clinical research professionals and research investigators to define the skills deemed essential for clinical research. One example is the Core Competencies in Clinical and Translational Research (CTSA, 2011). Developed for emerging clinical research investigators, the framework is composed of 15 core thematic areas relevant to different aspects of clinical research. Another example is the competency framework developed for clinical research professionals by the Joint Task Force (JTF) for clinical trial competency (Sonstein et al., 2014). Created in 2014 for clinical research professionals, this framework includes eight competency domains with 47 competency statements that describe specific skills and abilities related to clinical research. These frameworks have been widely used in clinical research training programs, and a validated assessment tool of the JTF competencies has been published (Hornung et al., 2018). Other examples of competency frameworks have been proposed in the literature. However, many have no accompanying assessment tools (Dilmore et al., 2013; Yoon, Park, Shin, & Ahn, 2018) or have assessment tools that have not been validated (Adkison & Glaros, 2012). These trends limit the utility of the competency frameworks developed for clinical research training programs.
The identification of clinical research competencies needs to be followed by the development of rigorous ways to assess clinical research skills (Laidlaw, Aiton, Struthers, & Guild, 2012). Assessment plays an important educational role by identifying areas for remedial action at the individual level and gives administrators feedback on curriculum design at the program level (Holmboe, Sherbino, Long, Swing, & Frank, 2010). Previous research has found that many of the published evaluations of clinical research training programs lack rigor (Mazmanian, Coe, Evans, Longo, & Wright, 2014). While the use of rigorously validated assessment tools would not entirely address this critique, it might serve to mitigate one potential limitation. The work presented here contributes to this agenda by identifying currently available assessment tools that are most relevant to the practice of clinical research and to compare their valid uses in order to identify opportunities for further assessment development.
We defined four research questions: (1) What assessment tools of clinical research competence currently exist? (2) Which competencies have been included in competency assessments? (3) How have relevant competency assessments been validated? (4) Can these assessment tools be used to measure the impact of clinical research training programs and experience on competence?
Method
To locate relevant articles, we conducted a systematic literature review (Grant & Booth, 2009; Petticrew & Roberts, 2006). For this review, we chose to focus on clinical research competencies for investigators, which is consistent with the CTSA focus on training research investigators. We included any relevant English-language articles published before December 31, 2018, in this review. Consistent with recommended practices (Haig & Dozier, 2003a, 2003b), we identified relevant databases to locate medical education literature and constructed search terms. The results were reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher, Liberati, Tetzlaff, Altman, & The Prisma Group, 2009). To ensure that our search could be replicated by other authors, we included only peer-reviewed articles published in indexed journals; other types of “gray” literature, such as conference presentations and proceedings, were excluded.
Search Terms
To locate articles, we identified several MeSH terms for use within the PubMed database. These terms were carefully selected in collaboration with the research team and outside experts, including a research librarian. We used a combination of several MeSH terms in our searches, for example: ((Biomedical Research/education [MeSH Terms]) AND (professional competence OR Competency-Based Education/methods [MeSH Terms])) AND (Educational Measurement OR Program evaluation [MeSH Terms]). We also used several Title/Abstract searches, including key words such as Clinical Research*, Assessment, Evaluation, Competenc*, Clinical, and Research*. Similar search terms were used to search for articles on the CINAHL, Scopus, and ERIC databases. A comprehensive list of search terms used on PubMed can be found in Supplementary Material Table S1.
Inclusion/Exclusion criteria
Clear review criteria were created to identify articles that met the objectives of this systematic review. We evaluated article abstracts to determine whether they met the following inclusion and exclusion criteria: Samples had to be composed mostly of clinical investigators or clinical investigators in training (i.e., medical students, postdoctoral researchers, and residents). Eligible disciplines included medicine, public health, nursing, pharmacy, dentistry, and other related clinical fields. Articles with samples composed primarily of undergraduate students, lab assistants, clinical research professionals, or nonclinical professionals who conduct research (e.g., social scientists) were excluded from this review. Articles had to focus on assessment tools measuring clinical research skills. Only studies that examined advanced research skills that would be expected of principal investigators were included in this review. Assessment tools that focused mostly on clinical practice skills were excluded. We also excluded assessment tools measuring clinicians’ attitudes toward research. Research-based competencies (or closely related terms like skills, abilities, mastery, knowledge) had to have been assessed in some way. This meant that articles that merely proposed a competency framework without some attempt at validation (Kane, 1992) were excluded.
If the abstract suggested that the article met all three criteria, the full article was retrieved and analyzed in-depth using a review matrix (Supplementary Material Table S2). When no additional relevant articles could be located, the search for articles stopped. Once a pool of potentially eligible articles was identified, the articles underwent peer review by several professionals experienced in clinical research, competency-based education, and assessment. To identify articles that eluded the initial literature search, we examined the reference section of these articles to locate any that had not been found during the literature search.
Data Abstraction
Our systematic literature review comprised three rounds of review (see Figure 1). The first author (P.A.I.) carried out the literature search and was solely responsible for identifying articles for potential inclusion. Initial review was carried out by P.A.I., and articles that appeared to meet the inclusion criteria were retained for further review. These articles then underwent a second round of review by a team (E.M.S., B.L.E., and T.E.P.) with experience in clinical research and research training. The team reviewed the articles separately using the aforementioned review matrix and provided scores according to the review criteria. A consensus of the three authors determined whether the article was included in the review. Articles on which there was no consensus were discussed as a group, and a unanimous final decision was made on whether the article should be included or excluded.
Diagram of review process of assessment tools used to evaluate clinical research competencies.
Results
Literature Review
Seventy-six articles that were judged to have met the inclusion criteria were selected and reviewed for eligibility. Several iterations of review led to the inclusion of 16 articles (Ameredes et al., 2015; Awaisu et al., 2015; Bakken, Sheridan, & Carnes, 2003; Bates et al., 2007; Cruser et al., 2010; Cruser et al., 2009; Ellis, McCreadie, McGregory, & Streetman, 2007; Jeffe et al., 2017; Lipira et al., 2010; Lowe et al., 2008; Mullikin, Bakken, & Betz, 2007; Murphy, Kalpakjian, Mullan, & Clauw, 2010; Patel, Tomich, Kent, Chaikof, & Rodrigue, 2018; Poloyac et al., 2011; Robinson et al., 2013; Streetman, McCreadie, McGregory, & Ellis, 2006).
To address the research questions outlined above, we organized the results into several sections including (1) sampling (size and participant type) and type of training program, (2) assessment type, (3) competency domains, (4) validation methods, and (5) assessment of impact of training or experience. Our ability to detect potential biases was limited because most of the studies reviewed were noninterventional studies that used unrelated survey questionnaires.
Sampling and Type of Training Program
Sample characteristics of the reviewed studies are shown in Table 1. Samples ranged in size from 12 to 394. Educational experience varied widely, with studies commonly including medical students, residents, postdoctoral students, and faculty. Eight articles only sampled from a single specialty, such as osteopathic medicine, surgery, pharmacy, and physical/occupational therapy. Only two studies explicitly sampled individuals from a wide spectrum of disciplines. The remaining six articles did not report the disciplines represented in their sample. Thirteen articles examined the effects of educational programs intended to increase clinical research competence. Research training programs took diverse forms including mentorship programs (n = 2), brief training programs (n = 2), single semester or yearlong courses (n = 5), graduate/postgraduate training programs (n = 3), and KL2/TL1 award programs (n = 1). Three studies did not examine the effects of a specific educational program, for differing reasons. In these studies, participants were active researchers, had been enrolled in a training program in the past, or had not yet taken a research training course.
Sample Characteristics for the 16 Studies Reviewed.
Assessment Type
The selected assessment tools fell into two broad categories—objective (knowledge tests) and subjective assessments (self-assessment surveys). Eight articles used only a self-assessment, two articles used only an objective test, and the remaining six articles employed both a self-assessment and an objective test; thus, 14 articles used subjective assessments and 8 articles used objective assessments (see Table 1). However, two articles used the same objective and subjective assessment tools (Ellis et al., 2007; Streetman et al., 2006), and two other studies used the same objective assessment tool (Cruser et al., 2010; Cruser et al., 2009), reducing the total number of discrete instruments to 13 subjective and 6 objective assessment tools.
Several types of self-assessment tools were developed and used. Much of the published research has focused on refining one particular assessment tool, the Clinical Research Appraisal Inventory (CRAI). Five versions of the CRAI were included in this systematic review. Each version had a different number of items, ranging from 12 to 88. The CRAI began as a 92-item instrument with 10 factors; subsequent studies have pared down both the number of items and the number of factors. In one study, the Research Self-Efficacy Scale (Greeley et al., 1989) was used, which is a general measure not specific to clinical researchers. The remaining self-assessments were measures of research confidence developed by the authors.
Eight articles used objective tests, with the majority of these using multiple-choice knowledge assessment tools. These tests generally focused on biostatistical competencies, with study design and research ethics being less frequently sampled. The remaining articles used a written format, either in the form of a comprehensive exam (Poloyac et al., 2011) or a research proposal and project report (Bates et al., 2007).
Competency Domains
Self-assessments of clinical research skills were typically organized into factors or competency domains (Figure 2). Ten assessment tools were organized into domains. The number of competency domains represented in each assessment tool ranged from 4 to 15. However, six studies did not organize clinical research skills into domains and instead used only an uncategorized list of specific skills. For this reason, these studies were not included in the totals shown in Figure 2. Although the competency domains used in some studies appeared to overlap more than domain, only one competency domain is shown in the figure to avoid double-counting.
Competency domains found in the literature, ordered from most to least common.
The most common competency domain assessed was study design, which was included in all 10 instruments. Competency domains related to ethics and scientific communication were common to all but one or two of the articles. Competency domains related to funding/grant writing, teamwork, conceptualizing hypotheses, and leadership/management were found in roughly half of the studies. The remaining competency domains shown in the figure were found in three or fewer studies.
Validation Methods
Evidence of validity was most commonly reported using internal consistency reliability and factor analysis. Internal consistency reliability, or the degree to which items of an assessment tool correlate with each other, was reported in seven studies (Awaisu et al., 2015; Cruser et al., 2009; Jeffe et al., 2017; Lipira et al., 2010; Lowe et al., 2008; Mullikin et al., 2007; Patel et al., 2018). Cronbach’s α values were generally high (i.e., α > 0.80). In contrast, test–retest reliability, or the stability of scores over time, was reported in only two studies (Mullikin et al., 2007; Streetman et al., 2006).
Some of the studies used factor analytic approaches to identify latent factors. Structural validity of the assessment tools was reported in five studies (Ameredes et al., 2015; Cruser et al., 2009; Lipira et al., 2010; Mullikin et al., 2007; Robinson et al., 2013). One study (Robinson et al., 2013) used an exploratory factor analysis (EFA) extraction method (principal factor); the other four (Ameredes et al., 2015; Cruser et al., 2009; Lipira et al., 2010; Mullikin et al., 2007) used a technique called principal components analysis (PCA), which is now considered outdated for the purpose of factor analysis. The number of factors was determined in different ways. Three articles (Ameredes et al., 2015; Lipira et al., 2010; Robinson et al., 2013) determined the number of factors in their assessment tools by extracting factors with associated eigenvalues greater than 1 (i.e., the Kaiser–Guttman rule; Kaiser, 1960), one study (Mullikin et al., 2007) used a set of five criteria to determine the number of factors, and one study (Cruser et al., 2009) did not indicate how the number of factors was determined. As a final step in rigorous validation studies, the confirmation of factor structure is needed, typically using a technique known as confirmatory factor analysis (CFA). This technique is used to test whether a factor model established in one data set can be replicated using new data (Levine, 2005). However, of the 16 articles included in this review, only one used CFA (Robinson et al., 2013).
Assessment of Impact of Training/Experience
We expected that scores on competency-based assessment tools would consistently be greater for individuals with more experience or training. In this study, we focused on the use of numeric scores used to demonstrate differences between groups. Many of the selected studies compared assessment scores across different learner groups and/or within one group of learners at the beginning and end of their educational programs. Three articles (Bates et al., 2007; Murphy et al., 2010; Poloyac et al., 2011) did not test group differences with statistical tests.
Seven of the articles we reviewed examined differences before and after training programs using significance testing. Three of these studies (Jeffe et al., 2017; Lipira et al., 2010; Patel et al., 2018) used self-assessments only, one study (Cruser et al., 2010) used objective assessments only, and three studies (Awaisu et al., 2015; Ellis et al., 2007; Lowe et al., 2008) used both assessment methods. Four studies (Awaisu et al., 2015; Ellis et al., 2007; Jeffe et al., 2017; Patel et al., 2018) reported statistically significant increases in trainees’ self-assessed competence on all or almost all skills or domains at the conclusion of the research training programs, whereas two (Lipira et al., 2010; Lowe et al., 2008) studies reported statistically significant increases for about half of the domains. However, there were fewer significant pretest-to-posttest increases on objective measures, based on the four studies that examined such differences (Awaisu et al., 2015; Cruser et al., 2010; Ellis et al., 2007; Lowe et al., 2008). Statistically significant increases in trainees’ objectively assessed competence were observed for 25% (Awaisu et al., 2015) to 35% (Cruser et al., 2010) of items and on one of two subscales (Lowe et al., 2008). However, significant decreases in scores were observed for some items in two studies (Cruser et al., 2010; Ellis et al., 2007).
Five articles (Ameredes et al., 2015; Cruser et al., 2009; Lipira et al., 2010; Mullikin et al., 2007; Streetman et al., 2006) examined differences in self-assessed competence between more and less experienced researchers. Three of these studies reported that individuals at a more advanced career stage reported significantly greater self-assessed competence than those who were less advanced on most domains (Lipira et al., 2010; Mullikin et al., 2007; Streetman et al., 2006). However, one study (Cruser et al., 2009) found that incoming students were more confident than second-year students, and another study (Ameredes et al., 2015) found no significant differences in self-assessed competence between more advanced and less advanced scholars for all except one competency domain. Only two articles (Cruser et al., 2009; Streetman et al., 2006) reported examining differences in objectively assessed competence between more and less experienced researchers. Both articles reported that more advanced scholars had significantly higher objectively assessed competence than less advanced scholars as measured by overall knowledge scores.
One study (Lowe et al., 2008) was unique in that it used a prepost quasi-experimental design. In this study, residents that were enrolled in a 1-year training program in clinical research at one university (intervention group) were compared to residents at two peer universities that did not offer structured training programs for clinical research (control group). They found that, compared to the control group, the intervention group had significantly greater improvement on two of the four self-assessment domains as well as one of the two subscales of the knowledge test.
Discussion
Our review of the literature on assessment tools measuring clinical researcher competence revealed several notable findings. For the studies reviewed, training appeared to have a much larger impact on self-assessed competence than on objective measures of competence. Although there were large pretest-to-posttest increases observed for self-assessed competence, there were fewer significant increases in research skills when competence was objectively assessed, even within the same study (Awaisu et al., 2015; Ellis et al., 2007). However, the reverse pattern was found for experience. Our review found no consistent relationship between self-assessed competence and professional experience; in contrast, experience was consistently associated with greater objectively assessed competence, although this was based on the results of only two studies. These findings are consistent with the Dunning–Kruger effect (Davis et al., 2006; Dunning, Heath, & Suls, 2004; Hodges, Regehr, & Martin, 2001), wherein individuals with lower levels of competence tend to rate themselves as being more highly competent. This finding also highlights the potential for misuse of self-assessments. These assessment tools are measures of self-efficacy, which is theorized to be an important determinant of career success in clinical and translational research (Lee et al., 2012; Rubio et al., 2011). However, these assessment tools should not be used as measures of actual competence, though this distinction was explicitly acknowledged in only one study included in this review (Robinson et al., 2013).
Studies in this review included both subjective and objective assessments, with some papers including both. Although self-assessments are easy to administer, they may not present an accurate picture of competence. In light of this, we consider the use of both subjective and objective assessments to be a promising method of program evaluation. However, the articles that used both subjective and objective measures had critical limitations that could restrict their application. In general, these studies suffered from limited validation, lack of factor structure in their objective tests, incompatibility between the self-assessment and objective assessment, lack of change in objective assessment scores from pretest to posttest, and limited content coverage.
There was no single, predominant competency framework across the studies included in this review. This may be one of the reasons why there is such poor overlap between the existing frameworks. Our review found that only three competency domains were found in nearly every classification scheme. These included study design, reporting/presenting, and research ethics. However, this is not intended to suggest that all necessary skills can be described solely by three competency domains. A combination of theory-based design, expert judgment, and rigorous, up-to-date factor analytic procedures is needed to establish content validity of an assessment tool (i.e., which competency domains ought to be included in a competency framework).
Finally, we found that the quality of validation of existing clinical research competency assessment tools varied greatly. While many of the studies provided evidence of validity, many of these claims were based on indirect and invalid measures of actual skill, as relatively few studies assessed the reliability of their instruments. Almost all of the instruments were used in only one study, which suggests that they may only be valid for use in very narrow contexts. Although the methods employed by some of the articles were rigorous, methodological flaws often limited the validation. There were consistent flaws in the approaches used to identify latent factors. PCA, which was used by four of five studies where factor analysis was undertaken, is not intended for the purpose of finding latent variables and is not an acceptable method of factor analysis (despite its common use for this purpose). Instead, it is generally recommended (Fabrigar, Wegener, MacCallum, & Strahan, 1999; Costello & Osborne, 2005) that researchers use EFA methods for the purpose of finding latent structure. In addition, many of these studies used the Kaiser–Guttman heuristic to extract factors, which tends to overrepresent the number of factors in comparison to recommended factor extraction techniques like scree plots and parallel analysis (Costello & Osborne, 2005).
Limitations
Although we conducted a structured review of the literature, it is possible that several articles were excluded incorrectly or missed altogether. Our search was limited in scope and thus excluded assessment tools that measured research competence developed in nonclinical fields that could potentially be adapted for use in assessing clinical research competencies. Furthermore, by restricting our searches to include only published articles, we may have missed validated assessment tools documented in gray literature or conference proceedings. This decision represents a scientific choice which benefits both the reproducibility of this work as well as the limits which must be placed on its implications.
In addition, the summary of studies examining the effects of training and experience was limited by the small number of studies examining such differences. For example, only two studies examined whether objectively assessed clinical research skills differed depending on experience. This is not sufficient to draw firm conclusions about the effects of experience on objectively assessed clinical research skills. More research will be needed to determine whether these findings can be corroborated, or if they are unique to these two studies.
Lastly, although the list of competencies shown in Figure 2 suggests the existence of a general set of knowledge, skills, and abilities, given the heterogeneity of clinical research investigator roles, it may be that those competencies vary depending on where the researcher works along the translational science spectrum. Bench science (T0) differs greatly from implementation research (T4), and it is possible that each step along the continuum requires a different set of competencies. This may partly explain the paucity of validated research on competencies among clinical investigators, and we suggest that future research should investigate whether such differences exist.
Recommendations for Future Research
Findings from this literature review have important implications for programmatic evaluation and improvement in medical education. First, we recommend assessment of research competencies by employing both objective and subjective measurements. Ideally, these should be grounded in the same theoretical framework to allow for comparisons between the two, which could be used to determine whether individuals have a realistic sense of their own ability in each competency domain. In addition, the use of pre/post testing would be useful in examining the effects of training programs (Calvin-Naylor et al., 2017).
In addition, we recommend that researchers perform validation of their measures using an appropriate sample of the population of interest (i.e., clinical investigators representing a wide variety of disciplines). This would allow the results to be applied more widely to other disciplines. Researchers should also consider the level of training of the sample (i.e., clinical investigators and students), which would allow for comparisons between groups (this is called known-groups validity). In addition, more studies need to be conducted that examine a broad range of defined research competencies. Researchers should identify tangible benchmarks for evaluation, including testing whether experience is related to competence or if competence improves after training (Shavelson, 2010, 2013).
Finally, we recommend that researchers apply appropriate statistical techniques to evaluate assessment tools. For example, a sufficient amount of data is needed to enable CFA testing following recommended guidelines. This could be done either by collecting data from two independent samples or by dividing a single large sample in two parts (one part used for EFA, the other for CFA). Researchers could then assess the convergent and discriminant validity of their instrument by administering measures of constructs thought to be related (or unrelated, in the case of discriminant validity) to the variable of interest.
Conclusions
As seen from this systematic review, more research needs to be done examining not only the validity of assessment measures but also a broader range of identified research skills for both summative and formative assessment. These findings have important implications for those interested in medical education and will contribute toward the critically important task of training the next generation of clinical and translational researchers.
Supplemental Material
Supplemental Material, Supplemental_Digital_Content_1_11252019 - Assessments of Research Competencies for Clinical Investigators: A Systematic Review
Supplemental Material, Supplemental_Digital_Content_1_11252019 for Assessments of Research Competencies for Clinical Investigators: A Systematic Review by Phillip A. Ianni, Elias M. Samuels, Brenda L. Eakin, Thomas E. Perorazio and Vicki L. Ellingrod in Evaluation & the Health Professions
Supplemental Material
Supplemental Material, Supplemental_Digital_Content_2_11252019 - Assessments of Research Competencies for Clinical Investigators: A Systematic Review
Supplemental Material, Supplemental_Digital_Content_2_11252019 for Assessments of Research Competencies for Clinical Investigators: A Systematic Review by Phillip A. Ianni, Elias M. Samuels, Brenda L. Eakin, Thomas E. Perorazio and Vicki L. Ellingrod in Evaluation & the Health Professions
Footnotes
Acknowledgments
We thank Dr. Paul Mazmanian, Dr. Susan Murphy, Dr. Carolynn Jones, and Dr. Carlton Hornung for their assistance in the presubmission review of our paper and for their helpful feedback and recommendations.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the National Center for Advancing Translational Science (NCATS), grant numbers U01TR002013, UL1TR002240, TL1TR002242, and KL2TR002241.
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References
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