Indigenous Peoples’ Experience and Understanding of Menstrual and Gynecological Health in Australia, Canada and New Zealand: A Scoping Review
Bibliographic record
Abstract
There are a variety of cultural and religious beliefs and customs worldwide related to menstruation, and these often frame discussing periods and any gynecological issues as taboo. While there has been previous research on the impact of these beliefs on menstrual health literacy, this has almost entirely been confined to low- and middle-income countries, with very little information on high-income countries. This project used the Joanna Briggs Institute (JBI) scoping review methodology to systematically map the extent and range of evidence of health literacy of menstruation and gynecological disorders in Indigenous people in the colonized, higher-income countries of Australia, Canada, and New Zealand. PubMed, CINHAL, PsycInfo databases, and the grey literature were searched in March 2022. Five studies from Australia and New Zealand met the inclusion criteria. Only one of the five included studies focused exclusively on menstrual health literacy among the Indigenous population. Despite considerable research on menstrual health globally, studies focusing on understanding the menstrual health practices of the Indigenous populations of Australia, New Zealand, and Canada are severely lacking, and there is little to no information on how Indigenous beliefs of colonized people may differ from the broader society in which they live.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".