PURPOSE: 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.
PURPOSE: 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: Louise M Henderson; Thad Benefield; Mary W Marsh; Bruce F Schroeder; Danielle D Durham; Bonnie C Yankaskas; J Michael Bowling Journal: Acad Radiol Date: 2014-11-27 Impact factor: 3.173
Authors: Louise M Henderson; Thad Benefield; J Michael Bowling; Danielle D Durham; Mary W Marsh; Bruce F Schroeder; Bonnie C Yankaskas Journal: AJR Am J Roentgenol Date: 2015-04 Impact factor: 3.959
Authors: Diana L Miglioretti; Rebecca Smith-Bindman; Linn Abraham; R James Brenner; Patricia A Carney; Erin J Aiello Bowles; Diana S M Buist; Joann G Elmore Journal: J Natl Cancer Inst Date: 2007-12-11 Impact factor: 13.506
Authors: Joann G Elmore; Sara L Jackson; Linn Abraham; Diana L Miglioretti; Patricia A Carney; Berta M Geller; Bonnie C Yankaskas; Karla Kerlikowske; Tracy Onega; Robert D Rosenberg; Edward A Sickles; Diana S M Buist Journal: Radiology Date: 2009-10-28 Impact factor: 11.105
Authors: Brian L Sprague; Ronald E Gangnon; Veronica Burt; Amy Trentham-Dietz; John M Hampton; Robert D Wellman; Karla Kerlikowske; Diana L Miglioretti Journal: J Natl Cancer Inst Date: 2014-09-12 Impact factor: 13.506