A methodological protocol for conducting a scoping review of health research on/by/with Indigenous women in North America
Bibliographic record
Abstract
BACKGROUND: Indigenous women in North America experience multiple inequities in terms of health and well-being when compared to non-Indigenous women and Indigenous men. In an effort to understand these health disparities, there has been a surge of research in the field of Indigenous women's health and well-being over the last 20 years. The objective of this study is to conduct a scoping review of the most current research in this field to determine which theoretical frameworks are being used to study which topics in Indigenous women's health and well-being in North America. METHODS: The scoping review protocol used was designed to follow an iterative six-step process as laid out by Arksey and O'Malley. Peer-reviewed, academic articles from the following databases were identified: Academic Search Complete, Native Health Database, Web of Science, Google Scholar, Bibliography of Native North America, Sociological Abstracts, Gender Watch, and Indigenous Peoples of North America. Two team members subsequently conducted two screens of titles and abstracts to include articles which focused exclusively on Indigenous women's health and well-being published between 2011 and 2021. The literature considered focused on Indigenous women's health and well-being and explicitly states their use of critical theoretical frameworks (e.g., Indigenous feminist, intersectionality, Indigenous resurgence, feminist, critical race) or community-based participatory research (CBPR). Data analysis will involve quantitative and qualitative descriptions. DISCUSSION: The results of our scoping review (in progress) will map out the current field of Indigenous women's health research. Our findings will highlight the theoretical frameworks operationalized in research on Indigenous women's health, identify gaps therein, and provide a basis for understanding how these theoretical lenses shape questions, methodologies, analysis, and implications of academic 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 | 0.340 | 0.323 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.031 | 0.029 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.086 | 0.020 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".