Evidence-based dust exposure prediction and/or control tools in occupational settings: A scoping review protocol

Workplace atmospheric exposure monitoring is the standard method to assess and control hazardous dust exposure; however, feasibility and cost constraints often limit its application. In recent decades, evidence-based tools supporting exposure modelling and control banding have been developed to aid in predicting and/or controlling occupational exposure to various contaminants. However, there is limited information on the availability and applicability of evidence-based tools for predicting and/or controlling occupational dust exposure, as well as on the methods for evaluating these tools across different exposure scenarios. Therefore, this planned scoping review aims to identify existing evidence-based tools for dust exposure predicting and/or controlling and to present evaluation approaches.

We will employ the scoping review methods developed by the Joanna Briggs Institute (JBI). The search will be conducted on PubMed, Scopus, and Web of Science databases, in addition to grey literature from the National Institute for Occupational Safety and Health (NIOSH) and advanced Google searches. Studies will be included if they report evidence-based tools for predicting and/or controlling dust exposure using quantitative or semi-quantitative designs and provide a detailed explanation of the methods used for tool development. There will be no restrictions on publication date or geographical location; however, only studies published in English will be considered. Studies focusing exclusively on dust exposure in environmental settings will be excluded. Each member of the review team will screen titles, abstracts, and full texts independently and in collaboration, based on the inclusion criteria. The extracted data will encompass details such as author, title, country, accessible platforms, method/tool names, intended users, types of dust, and occupational settings. Descriptions of the identified tools will include numerical data and narrative summaries to ensure a comprehensive overview.

OSF (https://doi.org/10.17605/OSF.IO/S6EZJ).