title: Characterizing the Mammography Technologist Workforce in North Carolina.
abstract: OBJECTIVE
  Mammography technologists' level of training, years of experience, and feedback on technique may play an important role in the breast-cancer screening process. However, information on the mammography technologist workforce is scant.
  
  
  METHODS
  In 2013, we conducted a survey mailed to 912 mammography technologists working in 224 facilities certified by the Mammography Quality Standards Act in North Carolina. Using standard survey methodology, we developed and implemented a questionnaire on the education and training, work experiences, and workplace interactions of mammography technologists. We aggregated responses using survey weights to account for nonresponse. We describe and compare lead (administrative responsibilities) and nonlead (supervised by another technologist) mammography technologist characteristics, testing for differences, using t-tests and χ(2) analysis.
  
  
  RESULTS
  A total of 433 mammography technologists responded (survey response rate = 47.5%; 95% confidence interval [CI]: 44.2%-50.7%), including 128 lead and 305 nonlead technologists. Most mammography technologists were non-Hispanic, white women; their average age was 48 years. Approximately 93% of lead and nonlead technologists had mammography-specific training, but <4% had sonography certification, and 3% had MRI certification. Lead technologists reported more years of experience performing screening mammography (P = .02) and film mammography (P = .03), more administrative hours (P < .0001), and more workplace autonomy (P = .002) than nonlead technologists. Nonlead technologists were more likely to report performing diagnostic mammograms (P = .0004) or other breast imaging (P = .001), discuss image quality with a peer (P = .013), and have frequent face-to-face interaction with radiologists (P = .03).
  
  
  CONCLUSIONS
  Our findings offer insights into mammography technologists' training and work experiences, highlighting variability in characteristics of lead versus nonlead technologists.
authors.forename: Louise M
authors.initials: LM
authors.lastname: Henderson
authors.forename: Mary W
authors.initials: MW
authors.lastname: Marsh
authors.forename: Thad
authors.initials: T
authors.lastname: Benefield
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Pearsall
authors.forename: Danielle
authors.initials: D
authors.lastname: Durham
authors.forename: Bruce F
authors.initials: BF
authors.lastname: Schroeder
authors.forename: J Michael
authors.initials: JM
authors.lastname: Bowling
authors.forename: Cheryl A
authors.initials: CA
authors.lastname: Viglione
authors.forename: Bonnie C
authors.initials: BC
authors.lastname: Yankaskas
mesh_terms: D017677:Age Distribution; D000488:Allied Health Personnel / Q000145:classification / Q000706:statistics & numerical data*; D004522:Educational Status; D006280:Health Workforce / Q000706:statistics & numerical data*; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D009657:North Carolina / Q000453:epidemiology; D044469:Racial Groups / Q000706:statistics & numerical data*; D017678:Sex Distribution; D013679:Technology, Radiologic / Q000193:education; D016526:Workload / Q000706:statistics & numerical data*
keywords: Mammography; technologist; variability; workforce