Identifying Key Predictors of Academic Achievement in a Longitudinal Medical Research Module.

Background: Research engagement among medical students is often hindered by perceived time constraints, lack of motivation, inadequate facilities, and insufficient supervisory guidance. Only 7% have published research papers. Project-based programs enhance research skills but frequently lack alignment between objectives, teaching methods, and evaluation. In the Rio Grande Valley, there is limited research on these issues. This study aims to analyze the performance of pre-clerkship students in a two-year longitudinal research course. Aim: We hypothesize that core independent variables (InVar) such as attitudes toward research (R-ATR), cognition (BCRS), self-efficacy, and competency scores will improve throughout the longitudinal Research Module. These InVar will be associated with latent performance strata as dependent variables (DepVar). Methods: Participants (n=59) will include medical students in their first and second years of the Research Module. Data will be collected at the beginning and end of each semester, including scores on R-ATR, BCRS, self-efficacy, and competency using the Kirkpatrick model. Both students and teachers will evaluate mentors, resources, and guidance provided. Academic grades throughout the Research Module will serve as dependent variables. Analysis: Data will be analyzed as a longitudinal panel to assess changes in R-ATR, BCRS, self-efficacy, and competency scores. We will then examine the latent strata of academic trajectories over two years, correlating these with the initial scores on R-ATR, BCRS, self-efficacy, and competency to determine their predictive value for academic performance. Conclusion: This study scope demonstrates that academic performance can be predicted based on initial InVar, measured at the beginning of the semester to identify students who may struggle with effective learning strategies. This predictive capability can facilitate targeted interventions to support student success in medical research education.

Publication Details

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ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley)
Published
2024-09-12
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Natural Products and Applications
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article

Identifying Key Predictors of Academic Achievement in a Longitudinal Medical Research Module.

ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley)
Natural Products and Applications
article

Identifying Key Predictors of Academic Achievement in a Longitudinal Medical Research Module.

article en

Abstract

Background: Research engagement among medical students is often hindered by perceived time constraints, lack of motivation, inadequate facilities, and insufficient supervisory guidance. Only 7% have published research papers. Project-based programs enhance research skills but frequently lack alignment between objectives, teaching methods, and evaluation. In the Rio Grande Valley, there is limited research on these issues. This study aims to analyze the performance of pre-clerkship students in a two-year longitudinal research course. Aim: We hypothesize that core independent variables (InVar) such as attitudes toward research (R-ATR), cognition (BCRS), self-efficacy, and competency scores will improve throughout the longitudinal Research Module. These InVar will be associated with latent performance strata as dependent variables (DepVar). Methods: Participants (n=59) will include medical students in their first and second years of the Research Module. Data will be collected at the beginning and end of each semester, including scores on R-ATR, BCRS, self-efficacy, and competency using the Kirkpatrick model. Both students and teachers will evaluate mentors, resources, and guidance provided. Academic grades throughout the Research Module will serve as dependent variables. Analysis: Data will be analyzed as a longitudinal panel to assess changes in R-ATR, BCRS, self-efficacy, and competency scores. We will then examine the latent strata of academic trajectories over two years, correlating these with the initial scores on R-ATR, BCRS, self-efficacy, and competency to determine their predictive value for academic performance. Conclusion: This study scope demonstrates that academic performance can be predicted based on initial InVar, measured at the beginning of the semester to identify students who may struggle with effective learning strategies. This predictive capability can facilitate targeted interventions to support student success in medical research education.

ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley)
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Natural Products and Applications
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