title: Community college pathways: improving the U.S. physician workforce pipeline.
abstract: OBJECTIVE
  To examine the association between participation in a community college (CC) pathway, medical school admission, and intentions to practice in underserved communities or work with minority populations.
  
  
  METHODS
  The authors performed cross-sectional analyses of the 2012 Association of American Medical Colleges matriculant and applicant files and the Matriculating Student Questionnaire to assess associations between student characteristics and participation in a CC pathway. They used logistic regression to estimate the association among CC pathway and acceptance to medical school, intention to practice in underserved areas, or intention to work with minority populations.
  
  
  RESULTS
  There were 40,491 applicants and 17,518 matriculants to U.S. MD-granting schools; about one-third used a CC. A higher proportion of underrepresented minority (URM) matriculants used CC pathways compared with whites. Applicants who attended a CC after high school and before a four-year university (First-CC) had lower odds of acceptance into medical school (adjusted odds ratio [AOR] = 0.68; 95% CI 0.61-0.75; P < .05). Compared with medical students who never attended a CC, First-CC matriculants were more likely to have parents without a college education (304/895 [34%] versus 1,683/12,598 [13%], P < .001) and higher odds of intentions to practice in underserved communities (AOR = 1.26; 95% CI 1.04-1.53; P < .05), after adjusting for covariates.
  
  
  CONCLUSIONS
  There is both high representation of URM students and higher prevalence of intention to work with underserved communities among CC pathway participants. These findings may be of interest to those seeking to enhance diversity in the physician workforce.
authors: Dr. Talamantes is a Robert Wood Johnson Foundation Clinical Scholars Program Fellow, Department of Veterans Affairs, University of California, Los Angeles, Los Angeles, California. Dr. Mangione is professor, Division of General Internal Medicine and Health Services Research, Department of Medicine, David Geffen School of Medicine at UCLA, and Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California. Ms. Gonzalez is a fourth-year medical student, David Geffen School of Medicine at UCLA, Los Angeles, California. Dr. Jimenez was a fourth-year medical student, David Geffen School of Medicine at UCLA, Los Angeles, California, at the time of the study. He is now a first-year resident, Department of Internal Medicine, University of California, San Francisco, San Francisco, California. Mr. Gonzalez is a community college counselor, San Jose City College, San Jose, California. Dr. Moreno is assistant professor, Department of Family Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California.
authors: Efrain
authors: E
authors: Talamantes
authors: Carol M
authors: CM
authors: Mangione
authors: Karla
authors: K
authors: Gonzalez
authors: Alejandro
authors: A
authors: Jimenez
authors: Fabio
authors: F
authors: Gonzalez
authors: Gerardo
authors: G
authors: Moreno
mesh_terms: D001741:Black or African American / Q000706:statistics & numerical data; D001213:Asian / Q000706:statistics & numerical data; D002321:Career Choice; D003430:Cross-Sectional Studies; D005006:Ethnicity / Q000706:statistics & numerical data*; D005260:Female; D006630:Hispanic or Latino / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008507:Medically Underserved Area; D008913:Minority Groups / Q000706:statistics & numerical data*; D010820:Physicians / Q000706:statistics & numerical data* / Q000600:supply & distribution; D012570:School Admission Criteria; D012574:Schools / Q000706:statistics & numerical data; D012577:Schools, Medical / Q000706:statistics & numerical data*; D013337:Students, Medical / Q000706:statistics & numerical data*; D014481:United States; D014495:Universities / Q000706:statistics & numerical data*; D044465:White People / Q000706:statistics & numerical data