Login / Signup

User opinions for the Veterinary Radiology & Ultrasound journal differ among demographic groups.

Jeryl C JonesJohanna J Lascano
Published in: Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association (2018)
Meeting the changing needs of journal users is important for veterinary editors. The objective of this prospective survey study was to analyze user opinions for the journal Veterinary Radiology & Ultrasound. An online survey was developed based on a consensus among the journal's Editor-in-Chief and Editorial Board members, an industrial organizational psychologist, Executive Council members for the journal's owner organization, representatives of the journal's publisher, and members of the authors' University Institutional Review Board. The online survey link was sent via email to members of the journal's five represented organizations and responses were collected from January 2016 to June 2016. The survey response rate was 38.5% (478 survey responses received/1241 emails sent). Private practitioners were significantly more likely than academicians to consider the reviewer feedback to be accurate (U = 5855, P < 0.05). Respondents from North America were significantly more likely than Europeans to consider the reviewer feedback to be insightful (U = 6212, P < 0.05). A majority of respondents (75.1%) agreed or strongly agreed that the journal should change to a double-blinded peer review system, which has been implemented. Perceptions of quality and satisfaction with the journal were highly correlated to each other (r = 0.68, P < 0.01) and positively correlated with respondent age. Findings indicated that opinions of Veterinary Radiology & Ultrasound users are diverse and differ among some demographic groups. These results may be used to guide future strategic planning to ensure that journal content and Editorial Board membership are representative of these diverse points of view.
Keyphrases
  • cross sectional
  • magnetic resonance imaging
  • healthcare
  • primary care
  • artificial intelligence
  • machine learning
  • working memory
  • clinical practice