An Indigenous-informed scoping review study methodology: advancing the science of scoping reviews
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.555 | 0.662 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.033 | 0.023 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.025 | 0.024 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".