Improving menstrual equity in the USA: perspectives from trans and non-binary people assigned female at birth and health care providers
Bibliographic record
Abstract
Menstruation research has largely focused on addressing menstrual management barriers facing cisgender women and girls in low and middle-income countries. Scant literature has assessed the menstrual management needs of trans and non-binary people assigned female at birth. To better understand these frequently invisibilised menstruation-related needs, we conducted a multi-method qualitative study in New York City which included: 17 in-depth interviews across trans and non-binary people (n = 10) and health care providers who serve them (n = 7); and seven anonymous post-interview participatory writing responses with trans and non-binary participants to further elucidate their lived experiences with menstrual management. Lack of health care provider transgender competency, public toilet design (i.e. gaps in cubicle doors and lack of in-cubicle menstrual product disposal bins), and the social dynamics of public toilets (i.e. work and school) were identified as significant barriers to managing menstruation safely and accessibly for trans and non-binary people. These findings have important implications for healthcare policy, public toilet legislation and advancing menstrual equity in the USA.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".