机器学习在医护人员职业风险沟通中的应用：范围综述方案

Use of machine learning in occupational risk communication for healthcare workers: protocol for scoping review.

作者信息

de Oliveira Gabriela Laudares Albuquerque, Bonow Clarice Alves, Rodrigues Itiberê de Oliveira Castellano, Geraldo Amanda Xavier

机构信息

PROGRAMA DE PÓS-GRADUAÇÃO EM ENFERMAGEM, Universidade Federal de Pelotas, Pelotas, Brazil

NURSING, Universidade Federal de Pelotas, Pelotas, RS, Brazil.

出版信息

BMJ Open. 2025 Jan 8;15(1):e088729. doi: 10.1136/bmjopen-2024-088729.

DOI:10.1136/bmjopen-2024-088729

PMID:39779257

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11749028/

Abstract

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).

摘要

引言

随着技术的发展，机器学习（ML）这一计算机科学分支的应用呈指数级增长，该分支旨在通过算法将计算机转变为决策主体。本方案源于探索在医护人员职业风险沟通与管理中应用机器学习的最佳实践的需求。

方法与分析

本范围综述方案详细介绍了将在学术数据库中进行的检索，这些数据库包括在线公共医学文献分析与检索系统、通过虚拟健康图书馆检索的医学文献分析与检索系统、拉丁美洲和加勒比卫生科学文献数据库、西太平洋地区医学索引、护理数据库和科学电子图书馆在线数据库、Scopus数据库、科学引文索引数据库、电气与电子工程师协会（IEEE）Xplore数字图书馆以及医学文摘数据库。本范围综述方案概述了一项综述的目标、方法和时间表，该综述将探索并梳理关于在医护人员风险沟通中使用机器学习的现有科学证据和知识。本方案遵循系统评价与Meta分析扩展版的首选报告项目（PRISMA-ScR）以及乔安娜·布里格斯研究所进行范围综述的指南。该综述的指导性问题是：机器学习在医护人员风险沟通中是如何应用的？检索将使用人群、概念和背景术语以及每个数据库定义的特定描述符。叙述性综合将描述该综述的主要主题和研究结果。本范围综述的结果将通过在国际同行评审科学期刊上发表进行传播。