title: The relationship of indebtedness, race, and gender to the choice of general or subspecialty pediatrics.
abstract: BACKGROUND
  Little research has examined indebtedness and the choice of continued subspecialty training. Concerns about a decline in the proportion of primary care physicians obliges medical educators to understand factors that influence the choice of subspecialty training.
  
  
  METHODS
  Survey data on 437 pediatricians who graduated between the years 1981 and 1987 were collected in 1991. Logistic regression was used to examine the influences of sex, race, graduation year, type of medical school, and educational debt (adjusted for inflation) on whether a pediatrician had trained in a subspecialty.
  
  
  RESULTS
  Three variables were associated with subspecialty training. Men and whites were significantly more likely to have trained in subspecialties, as were earlier graduates. Type of medical school and debt did not enter the equation.
  
  
  CONCLUSIONS
  Other variables were found to be more influential than indebtedness in the career decisions of primary care and subspecialty pediatricians. Distinguishing between subspecialties that have noticeably higher incomes and those that serve to enhance primary care pediatrics may be illuminating. That men and whites were more likely to train in subspecialty pediatrics suggests that financial considerations, if present, may be masked under other cultural and societal factors.
authors.affiliation: Division of Research on Health Policy, American Academy of Pediatrics, Elk Grove Village, IL 60009-0927.
authors.forename: S E
authors.initials: SE
authors.lastname: Brotherton
mesh_terms: D000328:Adult; D002321:Career Choice; D016001:Confidence Intervals; D004467:Economics; D004502:Education, Medical, Continuing / Q000191:economics / Q000706:statistics & numerical data*; D005006:Ethnicity; D005260:Female; D006801:Humans; D007182:Income; D008297:Male; D016017:Odds Ratio; D010372:Pediatrics / Q000191:economics / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012737:Sex Factors; D014481:United States