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JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts
INTRODUCTION: Racialized population groups have worse health outcomes across the world compared with non-racialized populations. Evidence suggests that collecting race-based data should be done to mitigate racism as a barrier to health equity, and to amplify community voices, promote transparency, a...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193044/ https://www.ncbi.nlm.nih.gov/pubmed/37192794 http://dx.doi.org/10.1136/bmjopen-2022-069753 |
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author | Quan, Cindy Clark, Nancy Costigan, Catherine L Murphy, Jill Li, Michael David, Anita Ganesan, Soma Guzder, Jaswant Cross, Barbara |
author_facet | Quan, Cindy Clark, Nancy Costigan, Catherine L Murphy, Jill Li, Michael David, Anita Ganesan, Soma Guzder, Jaswant Cross, Barbara |
author_sort | Quan, Cindy |
collection | PubMed |
description | INTRODUCTION: Racialized population groups have worse health outcomes across the world compared with non-racialized populations. Evidence suggests that collecting race-based data should be done to mitigate racism as a barrier to health equity, and to amplify community voices, promote transparency, accountability, and shared governance of data. However, limited evidence exists on the best ways to collect race-based data in healthcare contexts. This systematic review aims to synthesize opinions and texts on the best practices for collecting race-based data in healthcare contexts. METHODS AND ANALYSES: We will use the Joanna Briggs Institute (JBI) method for synthesizing text and opinions. JBI is a global leader in evidence-based healthcare and provides guidelines for systematic reviews. The search strategy will locate both published and unpublished papers in English in CINAHL, Medline, PsycINFO, Scopus and Web of Science from 1 January 2013 to 1 January 2023, as well as unpublished studies and grey literature of relevant government and research websites using Google and ProQuest Dissertations and Theses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement methodology for systematic reviews of text and opinion will be applied, including screening and appraisal of the evidence by two independent reviewers and data extraction using JBI’s Narrative, Opinion, Text, Assessment, Review Instrument. This JBI systematic review of opinion and text will address gaps in knowledge about the best ways to collect race-based data in healthcare. Improvements in race-based data collection, may be related to structural policies that address racism in healthcare. Community participation may also be used to increase knowledge about collecting race-based data. ETHICS AND DISSEMINATION: The systematic review does not involve human subjects. Findings will be disseminated through a peer-reviewed publication in JBI evidence synthesis, conferences and media. PROSPERO REGISTRATION NUMBER: CRD42022368270. |
format | Online Article Text |
id | pubmed-10193044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-101930442023-05-19 JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts Quan, Cindy Clark, Nancy Costigan, Catherine L Murphy, Jill Li, Michael David, Anita Ganesan, Soma Guzder, Jaswant Cross, Barbara BMJ Open Health Informatics INTRODUCTION: Racialized population groups have worse health outcomes across the world compared with non-racialized populations. Evidence suggests that collecting race-based data should be done to mitigate racism as a barrier to health equity, and to amplify community voices, promote transparency, accountability, and shared governance of data. However, limited evidence exists on the best ways to collect race-based data in healthcare contexts. This systematic review aims to synthesize opinions and texts on the best practices for collecting race-based data in healthcare contexts. METHODS AND ANALYSES: We will use the Joanna Briggs Institute (JBI) method for synthesizing text and opinions. JBI is a global leader in evidence-based healthcare and provides guidelines for systematic reviews. The search strategy will locate both published and unpublished papers in English in CINAHL, Medline, PsycINFO, Scopus and Web of Science from 1 January 2013 to 1 January 2023, as well as unpublished studies and grey literature of relevant government and research websites using Google and ProQuest Dissertations and Theses. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement methodology for systematic reviews of text and opinion will be applied, including screening and appraisal of the evidence by two independent reviewers and data extraction using JBI’s Narrative, Opinion, Text, Assessment, Review Instrument. This JBI systematic review of opinion and text will address gaps in knowledge about the best ways to collect race-based data in healthcare. Improvements in race-based data collection, may be related to structural policies that address racism in healthcare. Community participation may also be used to increase knowledge about collecting race-based data. ETHICS AND DISSEMINATION: The systematic review does not involve human subjects. Findings will be disseminated through a peer-reviewed publication in JBI evidence synthesis, conferences and media. PROSPERO REGISTRATION NUMBER: CRD42022368270. BMJ Publishing Group 2023-05-16 /pmc/articles/PMC10193044/ /pubmed/37192794 http://dx.doi.org/10.1136/bmjopen-2022-069753 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Health Informatics Quan, Cindy Clark, Nancy Costigan, Catherine L Murphy, Jill Li, Michael David, Anita Ganesan, Soma Guzder, Jaswant Cross, Barbara JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
title | JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
title_full | JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
title_fullStr | JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
title_full_unstemmed | JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
title_short | JBI systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
title_sort | jbi systematic review protocol of text/opinions on how to best collect race-based data in healthcare contexts |
topic | Health Informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10193044/ https://www.ncbi.nlm.nih.gov/pubmed/37192794 http://dx.doi.org/10.1136/bmjopen-2022-069753 |
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