Literature DB >> 34457661

Prematriculation Healthcare Employment Predicts Success in Clerkship Environment.

Lindsay C Strowd1, Hong Gao2, Mary Claire O'Brien3, Cynthia Burns4, Julie A Freischlag5,6, Roy E Strowd7, David Grier8, Timothy R Peters9.   

Abstract

PURPOSE: The average age of the matriculating medical student is increasing as more students take time between college and medical school. Increasing numbers of students are employed in the healthcare field during these gap years. Studies have explored the relationship between matriculation age and medical school performance with conflicting findings. The impact of prior healthcare employment (PHE) on future clerkship performance has not been explored. We hypothesize that medical school performance metrics would be higher for students with PHE than their peers.
METHODS: A retrospective review of four medical school classes at a single institution was conducted. Each student's admission application was examined to identify students with at least 6 months paid employment in a clinical healthcare position (i.e., pre-matriculation direct patient interaction, PHE cohort). Multiple medical school performance metrics were obtained for each student.
RESULTS: Of the 434 included students, 49 were PHE (11.29%) and percent of students with PHE trended up over time. MCAT scores, USMLE Step 1, and Step 2 CK scores were not different for PHE and non-PHE medical students. PHE students had significantly higher NBME subject exams, clinical clerkship scores, and cumulative year 3 performance.
CONCLUSIONS: Students who matriculate to medical school with prior healthcare employment outperform their peers in the clinical environment, possibly due to acquisition of knowledge or skills from their prior employment. These findings support students seeking paid healthcare experiences prior to medical school and have implications for pre-medicine advising, admissions, and medical school curricula. © International Association of Medical Science Educators 2019.

Entities:  

Keywords:  Clerkship performance; Medical school admissions; Prior healthcare employment

Year:  2019        PMID: 34457661      PMCID: PMC8368350          DOI: 10.1007/s40670-019-00859-2

Source DB:  PubMed          Journal:  Med Sci Educ        ISSN: 2156-8650


  7 in total

Review 1.  Assessing personal qualities in medical school admissions.

Authors:  Mark A Albanese; Mikel H Snow; Susan E Skochelak; Kathryn N Huggett; Philip M Farrell
Journal:  Acad Med       Date:  2003-03       Impact factor: 6.893

2.  Admission variables as predictors of PANCE scores in physician assistant programs: a comparison study across universities.

Authors:  Rose Higgins; Sharon Moser; Amy Dereczyk; Roberto Canales; Gloria Stewart; Colleen Schierholtz; Ted J Ruback; Jane McDaniel; James Van Rhee; Steve Arbuckle
Journal:  J Physician Assist Educ       Date:  2010

Review 3.  How effective are selection methods in medical education? A systematic review.

Authors:  Fiona Patterson; Alec Knight; Jon Dowell; Sandra Nicholson; Fran Cousans; Jennifer Cleland
Journal:  Med Educ       Date:  2016-01       Impact factor: 6.251

4.  The predictive validity of the MCAT for medical school performance and medical board licensing examinations: a meta-analysis of the published research.

Authors:  Tyrone Donnon; Elizabeth Oddone Paolucci; Claudio Violato
Journal:  Acad Med       Date:  2007-01       Impact factor: 6.893

5.  The rise of the medical scribe industry: implications for the advancement of electronic health records.

Authors:  George A Gellert; Ricardo Ramirez; S Luke Webster
Journal:  JAMA       Date:  2015-04-07       Impact factor: 56.272

6.  Admissions Variables: Predictors of Physician Assistant Student Success.

Authors:  Trenton Honda; Dipu Patel-Junankar; Robert Baginski; Rebecca Scott
Journal:  J Physician Assist Educ       Date:  2018-09

7.  When Assessment Data Are Words: Validity Evidence for Qualitative Educational Assessments.

Authors:  David A Cook; Ayelet Kuper; Rose Hatala; Shiphra Ginsburg
Journal:  Acad Med       Date:  2016-10       Impact factor: 6.893

  7 in total

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