Transversal Challenges

Science Communication

Peer Review

23-T09
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October 21st 2023
COURSE COORDINATORS
Helena Donato (MLS, MSIS) , 
João Subtil (MD, PhD)
COURSE PRESENTATION
Peer reviewing is the ground on which is built scientific communication credibility and relies on reviewers’ knowledge and experience on publishing. However, most reviewers must learn how to help authors increase the value of their submitted papers, and how to increase their journal’s credibility, by identifying flaws (and correcting them when possible), some minor, like biases, or writing / copying mistakes, statistical errors, or slicing, and some more serious, like fraud, conflicts of interest, plagiarism, or false claims.

In this course we’re teaching all the tools a reviewer must master:
  • general work (check for compliance with journals’ instructions for authors, orthography general quality, general relevance and acceptability, ethical compliance);
  • tools to exclude plagiarism, data manipulation, photo or picture misuse or tampering
  • evaluation of statistical analysis quality
  • exclude biases and common mistakes
Additionally, we will also discuss:
  • the editorial process
  • the role of the editor and reviewer in the peer review and decision-making process
  • how to conduct an internal peer review (by the editorial team)
  • what the editor is looking for - what makes the perfect reviewer
We are also guiding how to write the review, to avoid own mistakes and biases, and to be positive and help the author to publish the paper.

This course is helpful to both reviewers and writers, for these teaching will also help the author anticipate the critics and be proactive and produce a better manuscript before submission.
TARGET AUDIENCE
Medical Doctors, Editors, Nurses, Investigators, and other health professionals
LEARNING OBJECTIVES >> KNOWLEDGE AND SKILLS TO DEVELOP
  1. Evaluate general text quality
  2. Check for compliance with journals’ instructions for authors
  3. Master tools for orthography general quality
  4. Evaluate general relevance and acceptability
  5. Know how to recognize ethical compliance
  6. Master tools to exclude plagiarism
  7. Master tools to identify data manipulation
  8. Use tools to spot photo or picture misuse or tampering
  9. Evaluation of statistical analysis quality
  10. Exclude biases and common mistakes
  11. How to write the review, to avoid own mistakes and biases, and to be positive and help the author to publish the paper
  12. Use reviewer recognition tools
ADMISSION CRITERIA
CV


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