Evidence map›Paper›PMID 26614888›Full record

ArticleJournal of the American College of Radiology : JACR2015

Characterizing the Mammography Technologist Workforce in North Carolina.

Louise M Henderson, Mary W Marsh, Thad Benefield, Elizabeth Pearsall, Danielle Durham, Bruce F Schroeder, J Michael Bowling, Cheryl A Viglione, Bonnie C Yankaskas

Abstract read
In one paragraph

Article in Journal of the American College of Radiology : JACR, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Louise M Henderson
Mary W Marsh
Thad Benefield
Elizabeth Pearsall
Danielle Durham
Bruce F Schroeder
J Michael Bowling
Cheryl A Viglione
Bonnie C Yankaskas

Funding

UTILITY AND OUTCOMES FROM MAMMOGRAPHY IN NORTH CAROLINAU01CA070040 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YANKASKAS, BONNIE C · 1995 to 2011
$6.4M
Technologists Effect on the Accuracy of Mammography (TEAM)R01CA155342 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HENDERSON, LOUISE · 2012 to 2015
$1.2M
NCI NIH HHS R01 CA155342NCI NIH HHS R01CA155342NCI NIH HHS U01 CA070040NCI NIH HHS U01CA70040
6 · The paper itself

Abstract

purposeMammography 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.

methodsIn 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.

resultsA 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).

conclusionsOur findings offer insights into mammography technologists' training and work experiences, highlighting variability in characteristics of lead versus nonlead technologists.

Indexed as

Technology, RadiologicAge DistributionAllied Health PersonnelEducational StatusHealth WorkforceHumansMammographyNorth CarolinaRacial GroupsSex DistributionWorkloadMammographytechnologistvariabilityworkforce

Identifiers

PMID26614888
PMCPMC4743655

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.