Literature DB >> 20382499

National survey to identify subspecialties at risk for physician shortages in Canadian academic radiology departments.

Kai-Ling Ng1, Jo Yazer, Mohammed Abdolell, Peter Brown.   

Abstract

PURPOSE: To identify subspecialty fields in Canadian academic radiology departments that are at risk for future manpower shortages. To determine reasons for the potential shortages and suggest potential solutions.
METHODS: An anonymous online survey was sent by e-mail to radiology residents and academic radiology department heads in Canada. The survey was open from April 1 to August 1, 2006. Statistical analysis by using the SAS Frequency Procedure was performed on the results.
RESULTS: Interventional radiology, neuroradiology, mammography, cardiac imaging, and pediatric radiology were identified as areas in which there will be increasing workforce demands. Mammography, pediatric radiology, and cardiac imaging were identified as areas in which there will be a potential decrease in supply. Of the residents, 65.83% intended on pursuing subspecialty training. Priorities were interesting work, job availability, and work schedule. Nuclear medicine, mammography, pediatric radiology, and interventional radiology were identified as the top 4 areas in which residents specifically did not want to pursue further subspecialty training. Only 15% of resident respondents received career counseling during residency, and only 50% of those residents thought it was adequate.
CONCLUSIONS: Our survey results indicate that mammography, cardiac imaging, and pediatric radiology are at risk for manpower shortages, and interventional radiology may be at risk. Increased efforts to recruit trainees may be necessary to ensure that these subspecialties maintain their presence in the future. Only 15% of the surveyed residents received career counseling during residency. This is a relatively untapped forum that academic staff could use to help recruit new trainees into these underserved subspecialties. Crown
Copyright © 2010. Published by Elsevier Inc. All rights reserved.

Mesh:

Year:  2010        PMID: 20382499     DOI: 10.1016/j.carj.2010.02.007

Source DB:  PubMed          Journal:  Can Assoc Radiol J        ISSN: 0846-5371            Impact factor:   2.248


  6 in total

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5.  Segmentation of Medical Image Using Novel Dilated Ghost Deep Learning Model.

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6.  Factors Influencing the Choice of Radiology Subspecialty Among Radiology Trainees in Saudi Arabia.

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  6 in total

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