Marina Marin

Biographic Data

ID9647587
NAMEMarina Marin
GIVEN NAMESMarina
FAMILY NAMEMarin
SIGNATUREMARÍN M
AFFILIATIONSM. Marin is director, Division of Academic Analytics, Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York ; [email protected]
VERIFIEDNo
TOTAL WORKS6
TOTAL CITATIONS0
WORKS AS AUTHOR6
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2021
LATEST PUBLICATION YEAR2025
H-INDEX0

  • Outcomes of Accelerated 3-Year MD Graduates at NYU Grossman School of Medicine During Medical School and Early Residency

    Open AccessNivedha Satyamoorthi, Marina Marin et al.ARTICLEAcademic Medicine2025

  • Learner Assessment and Program Evaluation

    Open AccessJudee Richardson, Sally A Santen et al.ARTICLEAcademic Medicine2024

    Precision education (PE) systematically leverages data and advanced analytics to inform educational interventions that, in turn, promote meaningful learner outcomes. PE does this by incorporating analytic results back into the education continuum through continuous feedback cycles. These data-informed sequences of planning, learning, assessing, and adjusting foster competence and adaptive expertise. PE cycles occur at individual (micro), program …

  • Artificial Intelligence Screening of Medical School Applications

    Open AccessMarc M Triola, Ilan Reinstein et al.ARTICLEAcademic Medicine2023

  • A New Tool for Holistic Residency Application Review

    Open AccessArun Umesh Mahtani, Ilan Reinstein et al.ARTICLEAcademic Medicine2023

  • The AMA Graduate Profile

    Jesse Burk-Rafel, Marina Marin et al.ARTICLEAcademic Medicine2021

    Purpose: Medical schools aim to train competent physicians that meet the health care needs of society. Yet beyond the results of the residency match, medical schools often do not know what kind of workforce they are creating or how their graduates perform in practice. 1 Here, we describe the creation of a personalized Graduate Profile for 32 medical schools in the American Medical Association (AMA) Accelerating Change in Medical Education grant c…

  • Development and Validation of a Machine Learning-Based Decision Support Tool for Residency Applicant Screening and Review

    Jesse Burk-Rafel, Ilan Reinstein et al.ARTICLEAcademic Medicine2021

    PURPOSE: Residency programs face overwhelming numbers of residency applications, limiting holistic review. Artificial intelligence techniques have been proposed to address this challenge but have not been created. Here, a multidisciplinary team sought to develop and validate a machine learning (ML)-based decision support tool (DST) for residency applicant screening and review. METHOD: Categorical applicant data from the 2018, 2019, and 2020 resid…