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Nurses' Motivations, Barriers, and Facilitators to Engage in a Peer Review Process: A Qualitative Study Protocol.

Júlio Belo FernandesJosefa M M DomingosJohn DeanSónia FernandesRogério Manuel Ferrinho FerreiraCristina Rosa Soares Lavareda BaixinhoCidália CastroAida SimõesCatarina BernardesAna Silva AlmeidaSónia LoureiroNoélia FerreiraIsabel SantosCatarina Godinho
Published in: Nursing reports (Pavia, Italy) (2023)
Peer review supports the integrity and quality of scientific publishing. However, although it is a fundamental part of the publishing process, peer review can also be challenging for reviewers, editors, and other stakeholders. The present study aims to explore the nurses' motivations, barriers, and facilitators in engaging in a peer review process. This qualitative, descriptive exploratory study will be developed in partnerships with three research centers. Researchers followed the consolidated criteria for reporting qualitative research (COREQ) checklist to ensure the quality of this study protocol. According to the selection criteria, the purposive sampling will be used to recruit nurse researchers that act as peer reviewers for several scientific journals in various fields of knowledge. Interviews will be conducted until data have been sufficiently consistent with meeting the initial objectives. Researchers will develop a guide comprising a set of open-ended questions to collect participants' characteristics, descriptive review behavior, and perceptions regarding their motivations, barriers, and facilitators. Researchers will analyze data using an inductive process of content analysis with the help of the QDA Miner Lite database. Findings from this study will generate knowledge that may help stakeholders identify facilitating factors and barriers and guide the development of strategies to remove or minimize these barriers.
Keyphrases
  • healthcare
  • study protocol
  • randomized controlled trial
  • mental health
  • primary care
  • systematic review
  • electronic health record
  • emergency department
  • clinical trial
  • public health
  • adverse drug
  • open label
  • deep learning