Indigenous Women’s Maternal Health Care Experiences: A Protocol for a Scoping Review
Bibliographic record
Abstract
Introduction Indigenous women, infants, families, and communities experience higher than average health inequities and health disparities. The Public Health Agency of Canadas Maternity Experiences Survey excluded First Nations women living on-reserve, institutionalized (incarcerated) women and women whose children were not living with them (apprehended) at the time of the survey. Therefore, the survey did not fully capture Indigenous women's maternity experiences. It also failed to include the unique and complex context of Indigenous women’s epistemologies and maternal pedagogies or account for colonialism, racism, and sexism and the social contexts in which Indigenous women live. This scoping review will determine the extent to which research has been conducted on Indigenous women’s maternity experiences, will summarize the existing research, and will identify any gaps in the research that need to be addressed. Methods and analysis A comprehensive search will include: MEDLINE (OVID), CINAHL Plus, EMBASE, , AccessMedicine, WebUnited Nations digital library, SocINDEX, North American Indian Thought and Culture, Indigenous Peoples of North America, Early Encounters in North America, eHRAF World Cultures, Canadiana Online, Canadian Research Index, Anthropology Plus, America: History and Life, APA psycinfo, BIOSIS Previews. General searches will also be conducted in the following databases: Academic Search Complete, Cochrane Library, ScienceDirect, SAGE Journals, Wiley Online Library, SpringerLink, MLA international Bibliography, Gale OneFile: Gender Studies, Defining Gender, ProQuest Dissertations, Theses Canada, Theses Global, University of Calgary Theses and Dissertations, and Google Scholar. Qualitative, quantitative, and mixed methods studies that focus on Indigenous women’s maternal/child health experiences will be included. To establish eligibility of the publication titles and abstracts will be read and analysed by two independent reviewers. The same independent reviewers will read and assess the full text of each publication for inclusion and exclusion criteria. A data collection tool has been developed using Microsoft Excel software to assist the team in extracting studies relevant to this review.
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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 | 0.089 | 0.076 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.010 |
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".