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Use of machine learning in occupational risk communication for healthcare workers: protocol for scoping review
Gabriela Laudares Albuquerque de Oliveira1, Clarice Alves Bonow2, Itiberê de Oliveira Castellano Rodrigues3
1PROGRAMA DE PÓS-GRADUAÇÃO EM ENFERMAGEM, Universidade Federal de Pelotas, Pelotas, Brazil gabialbuquerque83@gmail.com.
Introduction:
With the development of technology, the use of machine learning (ML), a branch of computer science that aims to transform computers into decision-making agents through algorithms, has grown exponentially. This protocol arises from the need to explore the best practices for applying ML in the communication and management of occupational risks for healthcare workers.
Methods And Analysis:
This scoping review protocol details a search to be conducted in the academic databases, Public Medical Literature Analysis and Retrieval System Online, through the Virtual Health Library: Medical Literature Analysis and Retrieval System, Latin American and Caribbean Literature in Health Sciences, West Pacific Region Index Medicus, Nursing Database and Scientific Electronic Library Online, Scopus, Web of Science and IEEE Xplore Digital Library and Excerpta Medica Database. This scoping review protocol outlines the objectives, methods and timeline for a review that will explore and map the existing scientific evidence and knowledge on the use of ML in risk communication for healthcare workers. This protocol follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews and Joanna Briggs Institute guidelines for conducting scoping reviews. The guiding question of the review is: how is ML used in risk communication for healthcare workers? The search will use Population, Concept and Context terms and the specific descriptors defined by each database. The narrative synthesis will describe the main themes and findings of the review.The results of this scoping review will be disseminated through publication in an international peer-reviewed scientific journal.
Ethics And Dissemination:
Ethical approval is not required; data will rely on published articles. Findings will be published open access in an international peer-reviewed journal.
Trial Registration Number:
The protocol for this review was registered in the Open Science Framework under DOI 10.17605/OSF.IO/92SK4 (available at https://osf.io/92SK4).
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