Degendering Menstruation: A Scoping Review Exploring the Experiences of Transgender and Non-Binary People
Bibliographic record
Abstract
Menstruation is a biological process experienced by up to 800 million people on any given day. Historically, menstruation has been studied from the female perspective. However, it should be considered that not all who menstruate are women. Therefore, the purpose of this research was to determine the status of evidence on transgender and non-binary individuals' experiences with menstruation. Arksey and O'Malley's (2005) framework for conducting a scoping study was used to guide this review. The authors used five steps of the six-step process to identify the research problem and search strategy, select studies based on defined inclusion and exclusion criteria, extract key information from five selected studies, and chart, summarize, and report the results as themes. The analysis resulted in the identification of four themes: (1) gender dysphoria and the influence on identity; (2) menstrual management and transformation as a turning point; (3) managing menstruation in precarious spaces; and (4) moving toward an open dialogue. Findings suggest a need for awareness of diverse and inclusive menstrual experiences. Inclusive advertising and menstrual products are needed to support transgender and non-binary people and reduce gender dysphoria. Policy initiatives should support the reconceptualization of infrastructure so that bathrooms are safe and comfortable places. Future opportunities for research exploring menstrual management within transgender and non-binary populations with emphasis on global research with diverse cultures and social structures is necessary to address gaps in the existing literature.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".