Examining Summer Laboratory Research Apprenticeships for High School Students as a Factor in Entry to MD/PhD Programs at Matriculation

Tai, Robert H.; Kong, Xiaoqing; Mitchell, Claire; Dabney, Katherine P.; Read, Daniel M.; Jeffe, Donna B.; Andriole, Dorothy A.; Wathington, Heather D.

doi:10.1187/cbe.15-07-0161

Cited by 13 publications

(23 citation statements)

References 32 publications

(29 reference statements)

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“…Developing programmatic support systems for MD–PhD students to optimally navigate the transitions between program phases should serve to better promote these students’ efforts to achieve their career goals as physician-scientists. Prior research experience during high school or college is important to enrollment in MD–PhD programs ( Jeffe et al , 2014b ; Andriole et al , 2015 ; Tai et al , 2017 ) and to building and sustaining a more racially/ethnically diverse biomedical research workforce ( Tai et al , 2017 ). The present study shows that these prior research experiences, along with attracting more students to MD–PhD programs, also helped in students’ transition from MD to PhD training.…”

Section: Discussionmentioning

confidence: 99%

Transition Experiences in MD–PhD Programs

Chakraverty

Jeffe

Tai

2018

LSE

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MD–PhD training takes, on average, 8 years to complete and involves two transitions, an MD-preclinical to PhD-research phase and a PhD-research to MD-clinical phase. There is a paucity of research about MD–PhD students’ experiences during each transition. This study examined transition experiences reported by 48 MD–PhD students who had experienced at least one of these transitions during their training. We purposefully sampled medical schools across the United States to recruit participants. Semistructured interviews were audio-recorded and transcribed for analysis; items focused on academic and social experiences within and outside their programs. Using a phenomenological approach and analytic induction, we examined students’ transition experiences during their MD–PhD programs. Five broad themes emerged centering on multiple needs: mentoring, facilitating integration with students in each phase, integrating the curriculum to foster mastery of skills needed for each phase, awareness of cultural differences between MD and PhD training, and support. None of the respondents attributed their transition experiences to gender or race/ethnicity. Students emphasized the need for mentoring by MD–PhD faculty and better institutional and program supports to mitigate feelings of isolation and help students relearn knowledge for clinical clerkships and ease re-entry into the hospital culture, which differs substantially from the research culture.

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Section: Discussionmentioning

confidence: 99%

Transition Experiences in MD–PhD Programs

Chakraverty

Jeffe

Tai

2018

LSE

Self Cite

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“…Lichtenstein et al, 2009). Our understanding is also growing regarding pre-university educational settings and young peoples' decision-making processes surrounding the pursuit of an engineering education (Cass, Hazari, Cribbs, Sadler, & Sonnert, 2011;Godwin, Sonnert, & Sadler, 2016b;Tai et al, 2017).…”

Section: Limited Understanding Of Young Students' Aspirationsmentioning

confidence: 99%

Comparing students' engineering and science aspirations from age 10 to 16: Investigating the role of gender, ethnicity, cultural capital, and attitudinal factors

Moote

Archer

DeWitt

et al. 2019

J of Engineering Edu

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Background Women (along with minority ethnic and low-income communities) remain underrepresented in engineering, despite a thirty-year history of research and equality legislation. Compared to the US and other EU countries, this underrepresentation is particularly pronounced in the UK. While existing literature gives insights into factors shaping retention and progression in university engineering students, comparatively less is known about the development of school students' engineering aspirations.Purpose This paper contrasts science and engineering analyses to explore how relationships between background and attitudinal factors and aspirations change across primary and secondary school. We examine the relative influence of gender on aspirations in both science and engineering. Design/MethodWe draw on survey data with over 20,000 English students from the [project name] project. A multilevel regression approach is implemented to test for the effects of gender, ethnicity, and cultural capital on science and engineering aspirations.Results Gender is the main factor related to engineering aspirations while science aspirations are influenced by a broader range of factors. School-level factors become increasingly important for engineering aspirations. We also report evidence of the early distinctiveness of young women who aspire to engineering, in terms of their relatively high self-concept and motivations. ConclusionsThe association of engineering with masculinity is evident in aspirations from age 10 and students aspiring to engineering are distinctive in several respects. Efforts aimed at improving participation in engineering might more usefully focus on challenging the elitist culture and practices, which may influence student perceptions, rather than focusing on changing student aspirations directly.

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“…There are many examples of outcome data of this nature in DBER. For example, researchers have tested whether student characteristics influence: passing or failing a test [38] or a course [39], majoring or not majoring in physics [40], matriculating or not matriculating into medical or graduate school [41], or completing or not completing a science, technology, engineering, and math (STEM) Ph.D. [42].…”

Section: B Logistic Regression 1 When To Use Logistic Regressionmentioning

confidence: 99%

Beyond linear regression: A reference for analyzing common data types in discipline based education research

Theobald

Aikens

Eddy

et al. 2019

Phys. Rev. Phys. Educ. Res.

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This paper is part of the Focused Collection on Quantitative Methods in PER: A Critical Examination.] A common goal in discipline-based education research (DBER) is to determine how to improve student outcomes. Linear regression is a common technique used to test hypotheses about the effects of interventions on continuous outcomes (such as exam score) as well as control for student nonequivalence in quasirandom experimental designs. (In quasirandom designs, subjects are not randomly assigned to treatments. For example, when treatment is assigned by classroom, and observations are made on students, the design is quasirandom because treatment is assigned to classroom, not subject (students).) However, many types of outcome data cannot be appropriately analyzed with linear regression. In these instances, researchers must move beyond linear regression and implement alternative regression techniques. For example, student outcomes can be measured on binary scales (e.g., pass or fail), tightly bound scales (e.g., strongly agree to strongly disagree), or nominal scales (i.e., different discrete choices for example multiple tracks within a physics major), each necessitating alternative regression techniques. Here, we review extensions of linear modeling-generalized linear models (glms)-and specifically compare five glms that are useful for analyzing DBER data: logistic, binomial, proportional odds (also called ordinal; including censored regression), multinomial, and Poisson (including negative binomial, hurdle, and zero-inflated) regression. We introduce a diagnostic tool to facilitate a researcher's identification of the most appropriate glm for their own data. For each model type, we explain when, why, and how to implement the regression approach. When: we provide examples of the types of research questions and outcome data that would motivate this regression approach, including citations to articles in the DBER literature. Why: we name which linear regression assumption is violated by the data type. How: we detail implementation and interpretation of this modeling approach in R, including R syntax and code, and how to discuss the regression output in research papers. Code accompanying each analysis can be found in the online github repository that is associated with this paper (https://github.com/ejtheobald/BeyondLinearRegression). This paper is not an exhaustive review of regression techniques, nor does it review nonregression-based analyses. Rather, it aims to compile and summarize regression techniques useful for the most common types of DBER data and provide examples, citations, and heavily annotated R code so that researchers can easily implement the technique in their work.

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Examining Summer Laboratory Research Apprenticeships for High School Students as a Factor in Entry to MD/PhD Programs at Matriculation

Cited by 13 publications

References 32 publications

Transition Experiences in MD–PhD Programs

Transition Experiences in MD–PhD Programs

Comparing students' engineering and science aspirations from age 10 to 16: Investigating the role of gender, ethnicity, cultural capital, and attitudinal factors

Beyond linear regression: A reference for analyzing common data types in discipline based education research

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