An Indigenous-informed scoping review study methodology: advancing the science of scoping reviews.

BACKGROUND: Historically, Indigenous voices have been silent in health research, reflective of colonial academic institutions that privilege Western ways of knowing. However, Indigenous methodologies and methods with an emphasis on the active involvement of Indigenous peoples and centering Indigenous voices are gaining traction in health education and research. In this paper, we map each phase of our scoping review process and weave Indigenous research methodologies into Arksey and O'Malley's (2005) framework for conducting scoping reviews. METHODS: Guided by an advisory circle consisting of Indigenous Knowledge Keepers and allied scholars, we utilized both Indigenous and Western methods to conduct a scoping review. As such, a circle of Knowledge Keepers provided guidance and informed our work, while our methods of searching and scoping the literature remained consistent with PRISMA-ScR guidelines. In keeping with an Indigenous methodology, the scoping review protocol was not registered allowing for an organic development of the research process. RESULTS: We built upon Arksey and O'Malley's 5-stages and added an additional 3 steps for a combined 8-stage model to guide our research: (1) Exploration and Listening, (2) Doing the Groundwork, (3) Identifying and Refining the Research Question, (4) Identifying Relevant Studies, (5) Study Selection, (6) Mapping Data, (7) Collating, Summarizing and Synthesizing the Data, and lastly, (8) Sharing and Making Meaning. Engagement and listening, corresponding to Arksey and O'Malley (2005)'s optional "consultation stage," was embedded throughout, but with greater intensity in stages 1 and 8. CONCLUSION: An Indigenous approach to conducting a scoping review includes forming a team with a wide array of experience in both Indigenous and Western methodologies, meaningful Indigenous representation, and inclusion of Indigenous perspectives to shape the analysis and presentation of findings. Engaging Indigenous peoples throughout the entire research process, listening, and including Indigenous voices and perspectives is vital in reconciliation research, producing both credible and useable information for both Indigenous communities and academia. Our Indigenous methodology for conducting a scoping review can serve as a valuable framework for summarizing Indigenous health-related research.

De-identification of free text data containing personal health information: a scoping review of reviews.

Introduction: Using data in research often requires that the data first be de-identified, particularly in the case of health data, which often include Personal Identifiable Information (PII) and/or Personal Health Identifying Information (PHII). There are established procedures for de-identifying structured data, but de-identifying clinical notes, electronic health records, and other records that include free text data is more complex. Several different ways to achieve this are documented in the literature. This scoping review identifies categories of de-identification methods that can be used for free text data. Methods: We adopted an established scoping review methodology to examine review articles published up to May 9, 2022, in Ovid MEDLINE; Ovid Embase; Scopus; the ACM Digital Library; IEEE Explore; and Compendex. Our research question was: What methods are used to de-identify free text data? Two independent reviewers conducted title and abstract screening and full-text article screening using the online review management tool Covidence. Results: The initial literature search retrieved 3,312 articles, most of which focused primarily on structured data. Eighteen publications describing methods of de-identification of free text data met the inclusion criteria for our review. The majority of the included articles focused on removing categories of personal health information identified by the Health Insurance Portability and Accountability Act (HIPAA). The de-identification methods they described combined rule-based methods or machine learning with other strategies such as deep learning. Conclusion: Our review identifies and categorises de-identification methods for free text data as rule-based methods, machine learning, deep learning and a combination of these and other approaches. Most of the articles we found in our search refer to de-identification methods that target some or all categories of PHII. Our review also highlights how de-identification systems for free text data have evolved over time and points to hybrid approaches as the most promising approach for the future.